title: Video-T1: Test-Time Scaling for Video Generation
summary: 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
paper_authors._id: 67e226039cd910bee045e38f
paper_authors.name: Fangfu Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-25T09:07:42.279Z
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paper_authors.fullname: Fangfu Liu
paper_authors.type: user
paper_authors.user: Liuff23
paper_authors._id: 67e226039cd910bee045e390
paper_authors.name: Hanyang Wang
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paper_authors.fullname: Hanyang Wang
paper_authors.type: user
paper_authors.user: hanyang-21
paper_authors._id: 67e226039cd910bee045e391
paper_authors.name: Yimo Cai
paper_authors._id: 67e226039cd910bee045e392
paper_authors.name: Kaiyan Zhang
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paper_authors.fullname: Kaiyan Zhang
paper_authors.type: user
paper_authors.user: iseesaw
paper_authors._id: 67e226039cd910bee045e393
paper_authors.name: Xiaohang Zhan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-25T09:08:05.983Z
paper_authors._id: 6528fc319474946b8541b36f
paper_authors.avatarUrl: /avatars/08ea388cbcd7c0f1361980127a8d33c3.svg
paper_authors.fullname: Xiaohang Zhan
paper_authors.type: user
paper_authors.user: xhangzhan
paper_authors._id: 67e226039cd910bee045e394
paper_authors.name: Yueqi Duan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-25T09:08:11.759Z
paper_authors._id: 66c8131afafc0fc87ca99650
paper_authors.avatarUrl: /avatars/a6eeba2ccf011d5c9964fd38f85bd671.svg
paper_authors.fullname: Yueqi Duan
paper_authors.type: user
paper_authors.user: duanyueqi
paper_title: Video-T1: Test-Time Scaling for Video Generation
paper_summary: 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
paper_ai_summary: 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.
paper_ai_keywords: 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