Video-T1: Test-Time Scaling for Video Generation
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
67e226039cd910bee045e38f
Fangfu Liu
admin_assigned
2025-03-25T09:07:42.279Z
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Fangfu Liu
user
Liuff23
67e226039cd910bee045e390
Hanyang Wang
claimed_verified
2025-04-03T08:29:17.296Z
65c38f6c137aba2aee524989
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Hanyang Wang
user
hanyang-21
67e226039cd910bee045e391
Yimo Cai
67e226039cd910bee045e392
Kaiyan Zhang
claimed_verified
2025-03-25T08:20:20.614Z
60bc94cd85a3ab33829b6211
/avatars/b57d36c7577fbbb42ea5b963eef4144a.svg
Kaiyan Zhang
user
iseesaw
67e226039cd910bee045e393
Xiaohang Zhan
admin_assigned
2025-03-25T09:08:05.983Z
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Xiaohang Zhan
user
xhangzhan
67e226039cd910bee045e394
Yueqi Duan
admin_assigned
2025-03-25T09:08:11.759Z
66c8131afafc0fc87ca99650
/avatars/a6eeba2ccf011d5c9964fd38f85bd671.svg
Yueqi Duan
user
duanyueqi
Video-T1: Test-Time Scaling for Video Generation
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
Test-Time Scaling (TTS) in video generation improves video quality by adaptively sampling from noise space with feedback mechanisms, particularly demonstrated with the Tree-of-Frames method.
Test-Time Scaling (TTS) video generation test-time verifiers Gaussian noise space target video distribution linear search Tree-of-Frames (ToF) autoregressive text-conditioned video generation benchmarks