Note: Discussions, quantitative ablation studies and elaborations on our propositions can be found in Appendix.pdf shared under the same folder.

## Teaser

From real-world complex scenes to AI-generated videos, our method preserves identity fidelity and synthesizes plausible novel views by operating entirely in noise initialization phase.

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A corgi sits on a blue beach towel, holding a selfie stick with a GoPro

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Cat wearing thick round glasses sits on a crimson velvet armchair

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A masked figure in suit stands against the of a modern cityscape

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In a dimly lit room, two men are talking, another figure stands behind

## Motivations

Motivation experiments are conducted to highlight the identified problems and to demonstrate the effectiveness of the proposed solutions: K-RNR and Stochastic Latent Modulation.

### Zero Terminal SNR Collapse

To motivate the zero terminal SNR problem discussed in Section 4.1, we present a motivating example in the context of video inversion. This example highlights the limitations of standard video reconstruction methods and demonstrates the effectiveness of our proposed solution K-RNR.

### Original Video

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Reference Video

### DDIM Inversion

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Reverse diffusion process starts with DDIM inverted latent as the initial noise.

### Encoded Video + DDIM Inversion

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VAE encoded original video is noised with random standard‑normal noise through the formula: √at x0 + √(1 - at) · ε

### Random Noise + KV Caching

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Random standard‑normal noise initialization and Key–Value sharing of DDIM inverted latent along the sequence dimension.

### K-RNR[K=3]

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VAE encoded original video is noised with DDIM inverted latent with our noise initialization using order K=3.

### K-RNR[K=4]

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VAE encoded original video is noised with DDIM inverted latent with our noise initialization using order K=4.

### Original Video

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Reference Video

### DDIM Inversion

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Reverse diffusion process starts with DDIM inverted latent as the initial noise.

### Encoded Video + DDIM Inversion

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VAE encoded original video is noised with DDIM inverted latent through the formula: √at x0 + √(1 - at) · ε

### Random Noise + KV Caching

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Random standard‑normal noise initialization and Key–Value sharing of DDIM inverted latent along the sequence dimension.

### K-RNR[K=2]

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VAE encoded original video is noised with DDIM inverted latent with our noise initialization using order K=2.

### K-RNR[K=3]

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VAE encoded original video is noised with DDIM inverted latent with our noise initialization using order K=3.

### Approaches to Zero Terminal SNR Collapse Problem

This motivation experiment corresponding to Figure 2 in the paper and supporting section 4.1 demonstrates common workarounds for zero-terminal SNR collapse problem and how they fall short in Dynamic Video Synthesis task.

### Original Video

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Reference Video

### 3D Point Cloud Rendering

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3D point cloud rendering of the reference video with unseen regions.

### Strength: 0.88

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Strength parameter is set to 0.88 which shortens the diffusion path and results in the reconstruction of the unseen regions.

### Strength: 0.95

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Strength parameter is set to 0.95 which improves the propagation to unseen regions but causes identity drift.

### VAE + DDIM

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VAE encoded original video is noised with DDIM inverted latent through the formula: √at x0 + √(1 - at) · ε

### K-RNR + SLM

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Our K-RNR wih K=6 and Stochastic Latent Modulation preserves identity fidelity and completes newly visible regions with plausibly.

### Stochastic Latent Modulation Motivation

This motivation experiment corresponding to Figure 7 in the paper and supporting section 4.4 highlights how VAE-encoded latents and DDIM-inverted latents behave differently under physically implausible, out-of-distribution scenarios, shedding light on their respective capacities for scene representation.

### Original Video

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Reference Video

### 3D Point Cloud Rendering

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3D point cloud rendering of the reference video with unseen regions.

### Processed Mask

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Smoothed mask of the 3D point cloud render video.

### Filled Render Video

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Unseen regions in the processed mask are repeatedly filled with top-left 60x60 pixels of the reference video.

### Inv. + Recon. on Filled Render

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Both VAE encoding in the reverse diffusion and DDIM inversion is applied to the filled render video.

### Inv. on Render + Recon. on Filled Render

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VAE encoding in the reverse diffusion is applied to the filled render video, and DDIM inversion is applied to the original render video.

## Qualitative Results

Comprehensive showcase of our method's performance across diverse scenarios and challenging cases. All experiments are conducted with CogVideoX-5B I2V architecture.

### AI Generated Videos

Experiments demonstrating the identity and motion preservation capabilities of our method in Sora generated synthetic videos.

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A corgi sits on a blue beach towel, holding a selfie stick with a GoPro

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A corgi sits on a blue beach towel, holding a selfie stick with a GoPro

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A corgi sits on a blue beach towel, holding a selfie stick with a GoPro

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A corgi sits on a blue beach towel, holding a selfie stick with a GoPro

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A corgi sits on a blue beach towel, holding a selfie stick with a GoPro

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A corgi sits on a blue beach towel, holding a selfie stick with a GoPro

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Cat wearing thick round glasses sits on a crimson velvet armchair

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Cat wearing thick round glasses sits on a crimson velvet armchair

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Cat wearing thick round glasses sits on a crimson velvet armchair

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Cat wearing thick round glasses sits on a crimson velvet armchair

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Van Gogh sits at a grand wooden desk

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Van Gogh sits at a grand wooden desk

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A monkey wearing red cap and a puffy blue vest sits atop a bench

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A monkey wearing red cap and a puffy blue vest sits atop a bench

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A confident corgi walks along the shoreline

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A confident corgi walks along the shoreline

### Recapturing Movie Scenes

Experiments investigating the effectiveness of our method in real world scenarios, specifically in the context of movie scenes. Creating a dynamic view in movie scenes requires preserving the identity and complex mouth, hand, and body motions.

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A masked figure in suit stands against the of a modern cityscape

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A masked figure in suit stands against the of a modern cityscape

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Two men are engaged in a serious conversation in front of an ornate building

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Two men are engaged in a serious conversation in front of an ornate building

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A police officer in uniform, accompanied by two men in mid-20th century overcoats and hats

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Two men in mid-20th century formal attire, including overcoats and fedoras, are on a boat

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A cluttered mid-sized corporate office filled with standard office furnishings and supplies

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A man in a gray suit and striped tie is seen balancing a large, carved pumpkin on his head

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Two hobbits dressed in worn cloaks sit among rugged, rocky terrain under a muted, overcast light

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Two officers dressed in elaborate 18th-century naval uniforms stand in an outdoor port setting.

### OpenVid-1M Examples

Experiments focusing on the human faces and body motions. OpenVid-1M is a large-scale dataset of humans performing various actions. Creating a dynamic view in OpenVid-1M requires preserving the identity and complex mouth, hand, and body motions.

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A woman with light brown hair and a white headscarf is captured mid-song

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A middle-aged man with graying hair, dressed in a dark coat and a purple shirt

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A man in a beige suit and green shirt is seated at a table, engaging in a serious conversation

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A middle-aged man with glasses, dressed in a light pink shirt, is seen standing indoors, possibly in a living room

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A man with shoulder-length brown hair, wearing a dark blazer over a purple shirt, is seated in a professional setting

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A middle-aged man with a full beard and graying hair is seen speaking into a microphone with a serious expression

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A cluttered mid-sized corporate office filled with standard office furnishings and supplies

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A man with short, dark hair, wearing a black turtleneck sweater, is seated in the driver's seat of a car

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A woman with long, straight blonde hair and bangs is seated in a dark green booth, wearing a black top

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A man with dark curly hair and a serious expression, wearing black-rimmed glasses and a dark jacket over a light shirt,

## Ablations

Visual demonstrations of ablation studies showing the impact of different components in our method.

### Noise Initialization Ablations (x12)

Experiments demonstrating noise‑initialization strategies to observe how effectively they preserve the original video's spatial and temporal details.

### Original Video

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Reference Video

### 3D Point Cloud Rendering

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3D point cloud rendering of the reference video with unseen regions.

### Random Noise

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Random standard normal noise is directly used as the initial latent.

### Random Noise + KV Caching

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Random standard‑normal noise initialization and Key–Value sharing of DDIM inverted latent along the sequence dimension.

### Encoded Video + Random Noise

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VAE encoded original video is noised with random standard‑normal noise through the formula: √at x0 + √(1 - at) · ε

### DDIM Inversion

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DDIM inverted latent is directly used as the initial latent.

### Encoded Video + DDIM Inv.[K=1]

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VAE encoded original video is noised with DDIM inversion through the formula: √at x0 + √(1 - at) · ε

### K-RNR + SLM [K=2]

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VAE encoded original video is noised with DDIM inverted latent with our noise initialization using order K=2.

### K-RNR + SLM [K=3]

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VAE encoded original video is noised with DDIM inverted latent with our noise initialization using order K=3.

### K-RNR + SLM [K=4]

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VAE encoded original video is noised with DDIM inverted latent with our noise initialization using order K=4.

### K-RNR + SLM [K=5]

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VAE encoded original video is noised with DDIM inverted latent with our noise initialization using order K=5.

### K-RNR + SLM [K=6]

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VAE encoded original video is noised with DDIM inverted latent with our noise initialization using order K=6.

### Stochastic Latent Modulation Ablations (x6)

Experiments investigating effectiveness of Stochastic Latent Modulation (SLM) on inpainting missing unseen regions.

### Original Video

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Reference Video

### 3D Point Cloud Rendering

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3D point cloud rendering of the reference video with unseen regions.

### K-RNR without SLM[K=3]

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K-RNR with K=3 is applied without SLM in the noise initialization phase, yielding the reconstructions of unseen regions.

### K-RNR without SLM[K=4]

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K-RNR with K=4 is applied without SLM in the noise initialization phase, yielding the reconstructions of unseen regions.

### K-RNR + SLM [K=3]

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K-RNR with K=3 is applied with SLM in the noise initialization phase, yielding plausible inpainting of unseen regions.

### K-RNR + SLM [K=4]

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K-RNR with K=4 is applied with SLM in the noise initialization phase, yielding plausible inpainting of unseen regions.

### Adaptive K-RNR Ablations (x6)

Experiments investigating various scale adaptation strategies to DDIM inverted latent and their impact on the generated video.

### Original Video

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Reference Video

### 3D Point Cloud Rendering

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3D point cloud rendering of the reference video with unseen regions.

### Standardization

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DDIM inverted latent is standardized to minimum 0 and maximum 1 in the noise initialization phase.

### Normalization

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DDIM inverted latent is normalized to mean 0 and standard deviation in the noise initialization phase.

### K-RNR without Adaptive Normalization [K=20]

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K-RNR with K=20 is applied without adaptive normalization in the noise initialization phase, yielding high contrast generation.

### K-RNR with Adaptive Normalization [K=20]

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K-RNR with K=20 is applied with adaptive normalization in the noise initialization phase, yielding more natural generation.

## Qualitative Comparisons

Side-by-side comparisons of our method with state-of-the-art approaches across diverse scenarios.

### Original Video

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Reference Video

### 3D Point Cloud Rendering

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Render Video

### Generative Camera Dolly

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Stable Video Diffusion

### TrajectoryAttention

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Stable Video Diffusion

### Diffusion as Shader

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CogVideoX

### TrajectoryCrafter

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CogVideoX

### ReCamMaster

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Wan2.1

### Ours [K=3]

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CogVideoX

### Ours [K=6]

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CogVideoX

### Original Video

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Reference Video

### 3D Point Cloud Rendering

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Render Video

### Generative Camera Dolly

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Stable Video Diffusion

### TrajectoryAttention

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Stable Video Diffusion

### Diffusion as Shader

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CogVideoX

### TrajectoryCrafter

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CogVideoX

### ReCamMaster

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Wan2.1

### Ours [K=3]

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CogVideoX

### Ours [K=6]

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CogVideoX

### Original Video

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Reference Video

### 3D Point Cloud Rendering

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Render Video

### Generative Camera Dolly

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Stable Video Diffusion

### TrajectoryAttention

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Stable Video Diffusion

### Diffusion as Shader

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CogVideoX

### TrajectoryCrafter

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CogVideoX

### ReCamMaster

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Wan2.1

### Ours [K=2]

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CogVideoX

### Ours [K=3]

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CogVideoX

### Original Video

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Reference Video

### 3D Point Cloud Rendering

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Render Video

### Generative Camera Dolly

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Stable Video Diffusion

### TrajectoryAttention

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Stable Video Diffusion

### Diffusion as Shader

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CogVideoX

### TrajectoryCrafter

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CogVideoX

### ReCamMaster

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Wan2.1

### Ours [K=2]

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CogVideoX

### Ours [K=3]

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CogVideoX

### Original Video

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Reference Video

### 3D Point Cloud Rendering

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Render Video

### Generative Camera Dolly

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Stable Video Diffusion

### TrajectoryAttention

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Stable Video Diffusion

### Diffusion as Shader

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CogVideoX

### TrajectoryCrafter

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CogVideoX

### ReCamMaster

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Wan2.1

### Ours [K=2]

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CogVideoX

### Ours [K=6]

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CogVideoX

## Limitations

As our method is a training-free method, it inherits the limitations and biases of the pre-trained video base model. Here we demonstrate the limitations regarding the dynamic view generation task.

### 3D Point Cloud Rendering Artifacts

As the camera moves closer to the subject, the imperfection of 3D point cloud rendering become more pronounced and results in body deformations.

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A young woman with medium-length dark hair and a concerned expression is seated in the driver's seat of a car

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A woman with a concerned expression is initially seen in a close-up, her dark hair pulled back to highlight her features

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A middle-aged man with graying hair, dressed in a dark coat and a purple shirt

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A woman with long, straight blonde hair and bangs is seated in a dark green booth, wearing a black top