Video-T1: Test-Time Scaling for Video Generation
Authors: name: Fangfu Liu, name: Hanyang Wang, name: Yimo Cai, name: Kaiyan Zhang, name: Xiaohang Zhan, name: Yueqi Duan

With the scale capability of increasing training data, model size, and
computational cost, video generation has achieved impressive results in digital
creation, enabling users to express creativity across various domains.
Recently, researchers in Large Language Models (LLMs) have expanded the scaling
to test-time, which can significantly improve LLM performance by using more
inference-time computation. Instead of scaling up video foundation models
through expensive training costs, we explore the power of Test-Time Scaling
(TTS) in video generation, aiming to answer the question: if a video generation
model is allowed to use non-trivial amount of inference-time compute, how much
can it improve generation quality given a challenging text prompt. In this
work, we reinterpret the test-time scaling of video generation as a searching
problem to sample better trajectories from Gaussian noise space to the target
video distribution. Specifically, we build the search space with test-time
verifiers to provide feedback and heuristic algorithms to guide searching
process. Given a text prompt, we first explore an intuitive linear search
strategy by increasing noise candidates at inference time. As full-step
denoising all frames simultaneously requires heavy test-time computation costs,
we further design a more efficient TTS method for video generation called
Tree-of-Frames (ToF) that adaptively expands and prunes video branches in an
autoregressive manner. Extensive experiments on text-conditioned video
generation benchmarks demonstrate that increasing test-time compute
consistently leads to significant improvements in the quality of videos.
Project page: https://liuff19.github.io/Video-T1