SV4D: Dynamic 3D Content Generation with Multi-Frame and Multi-View Consistency

🌈 Abstract

The paper presents Stable Video 4D (SV4D), a latent video diffusion model for multi-frame and multi-view consistent dynamic 3D content generation. Unlike previous methods that rely on separately trained generative models for video generation and novel view synthesis, SV4D is a unified diffusion model that can generate novel view videos of dynamic 3D objects from a monocular reference video. SV4D generates novel views for each video frame that are temporally consistent. The generated novel view videos are then used to efficiently optimize an implicit 4D representation (dynamic NeRF), without the need for cumbersome SDS-based optimization used in most prior works.

🙋 Q&A

[01] Stable Video 4D (SV4D)

1. What is the key idea behind SV4D? The key idea behind SV4D is to build multi-frame and multi-view consistency in a 4D object by combining the frame-consistency in a video diffusion model (SVD) with the multi-view consistency in a multi-view diffusion model (SV3D) within a unified architecture.

2. How does SV4D generate novel view videos? SV4D takes a monocular reference video as input and generates novel view videos that are temporally consistent. It conditions the image matrix generation on the corresponding frames of the monocular input video as well as the views from reference multi-view images of the first video frame.

3. How does SV4D optimize the 4D representation? SV4D uses the generated novel view videos to efficiently optimize an implicit 4D representation (dynamic NeRF), without the need for cumbersome SDS-based optimization used in most prior works.

4. What are the key components of the SV4D network architecture? The SV4D network is built upon SVD and SV3D models. It consists of a UNet with multiple layers, where each layer contains a sequence of one residual block with Conv3D layers and three transformer blocks with spatial, view, and frame attention layers.

5. How does SV4D handle long input videos during inference? Due to memory limitations, SV4D cannot generate all the novel view frames at once when the input video is long. To address this, SV4D uses a novel mixed sampling scheme that first generates a sparse set of anchor frames, and then uses these anchor frames to densely sample the remaining frames, ensuring temporal consistency.

[02] Experiments

1. What datasets were used to evaluate SV4D? SV4D was evaluated on the synthetic datasets ObjaverseDy and Consistent4D, as well as the real-world DAVIS dataset.

2. How did SV4D perform compared to the baselines in novel view video synthesis? SV4D outperformed the baseline methods (SV3D, Diffusion2, STAG4D) in terms of both video frame consistency (lower FVD-F) and multi-view consistency (lower FVD-V, FVD-Diag, FV4D).

3. How did SV4D perform compared to the baselines in 4D generation? SV4D consistently outperformed the baseline methods (Consistent4D, STAG4D, DreamGaussian4D, etc.) in terms of visual quality (LPIPS, CLIP-S), video frame consistency (FVD-F), and multi-view consistency (FVD-V, FVD-Diag, FV4D).

4. What was the key advantage of SV4D's sampling scheme compared to using off-the-shelf video interpolation? SV4D's mixed sampling scheme, which generates a sparse set of anchor frames and then densely samples the remaining frames, was shown to produce more temporally consistent results compared to using an off-the-shelf video interpolation method to fill in the missing frames.