SV4D: Dynamic 3D Content Generation with Multi-Frame and Multi-View Consistency
Yiming Xie, Chun-Han Yao, Vikram Voleti, Huaizu Jiang, Varun Jampani
2024-07-25
Summary
This paper introduces SV4D, a new model designed for generating dynamic 3D content in videos. It focuses on creating consistent views and frames of 3D objects, allowing for more realistic animations and visual effects.
What's the problem?
Many current methods for creating videos of 3D objects struggle to maintain consistency across different frames and camera angles. Traditional approaches often require separate models for generating video and synthesizing new views, which can lead to issues with quality and coherence. This makes it difficult to produce smooth and realistic animations, especially when the perspective changes.
What's the solution?
SV4D addresses these challenges by using a single, unified model that generates new video frames while ensuring that the views of the 3D objects remain consistent over time. It takes a reference video as input and produces novel views for each frame in a way that keeps the motion smooth and coherent. The model also optimizes a special representation called dynamic NeRF (Neural Radiance Fields) efficiently, which helps create high-quality 3D visuals without the complex processes used in previous methods. The researchers built their model using a curated dataset of dynamic 3D objects to train it effectively.
Why it matters?
This research is important because it enhances the ability to create realistic 3D animations for various applications, such as video games, movies, and virtual reality. By improving how dynamic content is generated, SV4D can lead to better visual storytelling and more engaging experiences for users.
Abstract
We present 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, we design a unified diffusion model to generate novel view videos of dynamic 3D objects. Specifically, given a monocular reference video, SV4D generates novel views for each video frame that are temporally consistent. We then use the generated novel view videos to optimize an implicit 4D representation (dynamic NeRF) efficiently, without the need for cumbersome SDS-based optimization used in most prior works. To train our unified novel view video generation model, we curated a dynamic 3D object dataset from the existing Objaverse dataset. Extensive experimental results on multiple datasets and user studies demonstrate SV4D's state-of-the-art performance on novel-view video synthesis as well as 4D generation compared to prior works.