At the core of Video-T1 is a dual-strategy search framework that utilizes both random linear search and an efficient Tree-of-Frames (ToF) method. The random linear search samples multiple noise candidates in parallel, generating video clips and selecting the best based on test-time verifiers. Recognizing the computational demands of this method, the ToF strategy offers a more efficient alternative by adaptively expanding and pruning video branches in an autoregressive manner. This allows Video-T1 to balance computational cost with generation quality, making it possible to achieve high-quality results even with limited resources. The integration of test-time verifiers ensures that each generated video is evaluated for alignment with textual prompts, motion stability, and overall quality.
Video-T1's test-time scaling approach has been extensively validated on text-conditioned video generation benchmarks, consistently demonstrating substantial improvements in both objective metrics and human preference alignment. The framework is especially effective for common prompt sets and categories like scene and object depiction, delivering richer content expression and higher imaging quality. While improvements in challenging aspects such as motion smoothness and temporal flickering are more modest, Video-T1 still represents a major advancement in the field. Its open-source implementation supports multi-GPU inference and is designed for researchers and developers seeking to push the boundaries of digital video creation.
Key features include:
- Test-Time Scaling (TTS) for enhanced video generation quality
- Random linear search and Tree-of-Frames (ToF) strategies for efficient inference
- Test-time verifiers for evaluating prompt alignment and video quality
- Significant quality improvements without model retraining
- Supports multi-GPU inference for large-scale video generation
- Open-source framework suitable for researchers and developers