{"work":{"id":"b339d9de-dcbe-410b-a71b-079ed27d3c88","openalex_id":null,"doi":null,"arxiv_id":"2407.07667","raw_key":null,"title":"VEnhancer: Generative Space-Time Enhancement for Video Generation","authors":null,"authors_text":"J","year":2024,"venue":"cs.CV","abstract":"We present VEnhancer, a generative space-time enhancement framework that improves the existing text-to-video results by adding more details in spatial domain and synthetic detailed motion in temporal domain. Given a generated low-quality video, our approach can increase its spatial and temporal resolution simultaneously with arbitrary up-sampling space and time scales through a unified video diffusion model. Furthermore, VEnhancer effectively removes generated spatial artifacts and temporal flickering of generated videos. To achieve this, basing on a pretrained video diffusion model, we train a video ControlNet and inject it to the diffusion model as a condition on low frame-rate and low-resolution videos. To effectively train this video ControlNet, we design space-time data augmentation as well as video-aware conditioning. Benefiting from the above designs, VEnhancer yields to be stable during training and shares an elegant end-to-end training manner. Extensive experiments show that VEnhancer surpasses existing state-of-the-art video super-resolution and space-time super-resolution methods in enhancing AI-generated videos. Moreover, with VEnhancer, exisiting open-source state-of-the-art text-to-video method, VideoCrafter-2, reaches the top one in video generation benchmark -- VBench.","external_url":"https://arxiv.org/abs/2407.07667","cited_by_count":null,"metadata_source":"pith","metadata_fetched_at":"2026-07-08T07:34:43.070007+00:00","pith_arxiv_id":"2407.07667","created_at":"2026-05-12T05:26:23.768161+00:00","updated_at":"2026-07-08T07:34:43.070007+00:00","title_quality_ok":true,"display_title":"Venhancer: Generative space-time enhancement for video generation","render_title":"Venhancer: Generative space-time enhancement for video generation"},"hub":{"state":{"work_id":"b339d9de-dcbe-410b-a71b-079ed27d3c88","tier":"hub","tier_reason":"10+ Pith inbound or 1,000+ external citations","pith_inbound_count":17,"external_cited_by_count":null,"distinct_field_count":2,"first_pith_cited_at":"2024-10-07T17:56:04+00:00","last_pith_cited_at":"2026-07-07T15:24:42+00:00","author_build_status":"not_needed","summary_status":"needed","contexts_status":"needed","graph_status":"needed","ask_index_status":"not_needed","reader_status":"not_needed","recognition_status":"not_needed","updated_at":"2026-08-20T23:29:51.339136+00:00","tier_text":"hub"},"tier":"hub","role_counts":[{"context_role":"background","n":1}],"polarity_counts":[{"context_polarity":"background","n":1}],"runs":{},"summary":{},"graph":{},"authors":[]}}