4Real-Video-V2: Fused View-Time Attention and Feedforward Reconstruction for 4D Scene Generation
This addresses the problem of generating high-quality 4D scenes (3D+time) for computer vision and graphics applications, representing a novel architectural advancement.
The authors tackled 4D scene generation by proposing a fused attention architecture and enhanced reconstruction model, establishing new state-of-the-art results with improved visual quality and reconstruction capability.
We propose the first framework capable of computing a 4D spatio-temporal grid of video frames and 3D Gaussian particles for each time step using a feed-forward architecture. Our architecture has two main components, a 4D video model and a 4D reconstruction model. In the first part, we analyze current 4D video diffusion architectures that perform spatial and temporal attention either sequentially or in parallel within a two-stream design. We highlight the limitations of existing approaches and introduce a novel fused architecture that performs spatial and temporal attention within a single layer. The key to our method is a sparse attention pattern, where tokens attend to others in the same frame, at the same timestamp, or from the same viewpoint. In the second part, we extend existing 3D reconstruction algorithms by introducing a Gaussian head, a camera token replacement algorithm, and additional dynamic layers and training. Overall, we establish a new state of the art for 4D generation, improving both visual quality and reconstruction capability.