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.
Pipeline of Test-Time Scaling for Video Generation. Top: Random Linear Search for TTS video generation is to randomly sample Gaussian noises, prompt the video generator to generate sequential of video clips through step-by-step denoising in a linear manner, and select the highest score form the test verifiers. Bottom: Tree of Frames (ToF) Search for TTS video generation is to divide the video generation process into three stages: (a) the first stage performs image-level alignment that influences the later frames; (b) the second stage is to apply dynamic prompt in test verifiers to focus on motion stability, physical plausibility to provide feedback that guides heuristic searching process; (c) the last stage assesses the overall quality of the video and select the video with highest alignment with text prompts.
Test-Time Scaling for Video Generation. As the number of samples in the search space increases by scaling test-time computation (TTS), the models' performance exhibits consistent improvement (In the bar chart, light colors correspond to the results without TTS, while dark colors represent the improvement after TTS.).
TTS consistently yields stable performance gains across different video generation models. We conducted a series of random linear search experiments across multiple video generation models using different verifiers. Experiment results demonstrate that as the inference computational budget increases, all video generation models exhibit improved performance across different verifiers, eventually approaching a convergence limit once a certain threshold is reached. This finding indicates that the TTS strategy can effectively guide the search process during test time and significantly enhance generation quality. Moreover, when comparing different verifiers applied to the same video model, we observe varying growth rates and extents in their performance curves. This divergence suggests that each verifier emphasizes different evaluation aspects.
Performance across most dimensions can be greatly improved with TTS. The complexity of prompts across diverse benchmark dimensions is a key component in video TTS. We conduct experiments to quantitatively evaluate the performance improvement of different models using TTS methods across various dimensions. As ToF and random linear search can achieve a similar convergence score during test-time scaling, we choose the better score for (+TTS). We find that for common prompt sets (e.g., Scene, Object) and easily assessable categories (e.g., Imaging Quality), TTS methods achieve significant improvements across different models.
A few dimensions heavily rely on the capabilities of foundation models, making improvements challenging for TTS. However, for some hard-to-evaluate latent properties (e.g., Motion Smoothness, Temporal Flickering), the improvement is less pronounced. This is likely because Motion Smoothness requires precise control of motion trajectories across frames, which is challenging for current video generation models to achieve. Temporal Flickering, on the other hand, involves maintaining consistent appearance and intensity over time, which is difficult to precisely assess, especially when dealing with complex scenes and dynamic objects.
@article{liu2025video,
title={Video-T1: Test-Time Scaling for Video Generation},
author={Liu, Fangfu and Wang, Hanyang and Cai, Yimo and Zhang, Kaiyan and Zhan, Xiaohang and Duan, Yueqi},
journal={arXiv preprint arXiv:2503.18942},
year={2025}
}