{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/s2d-lfe-sparse-to-dense-light-field-event","title":"S2D-LFE: Sparse-to-Dense Light Field Event Generation","arxiv_id":null,"date":"2025-01-01","proceeding":"CVPR 2025 1","authors":["Yutong Liu","Wenming Weng","Yueyi Zhang","Zhiwei Xiong"],"abstract":"    In this paper, we present S2D-LFE, an innovative approach for sparse-to-dense light field event generation. For the first time to our knowledge, S2D-LFE enables controllable novel view synthesis only from sparse-view light field event (LFE) data, and addresses three critical challenges for the LFE generation task: simplicity, controllability, and consistency. The simplicity aspect eliminates the dependency on frame-based modality, which often suffers from motion blur and low frame-rate limitations. The controllability aspect enables precise view synthesis under sparse LFE conditions with view-related constraints. The consistency aspect ensures both cross-view and temporal coherence in the generated results. To realize S2D-LFE, we develop a novel diffusion-based generation network with two key components. First, we design an LFE-customized variational auto-encoder that effectively compresses and reconstructs LFE by integrating cross-view information. Second, we design an LFE-aware injection adaptor to extract comprehensive geometric and texture priors. Furthermore, we construct a large-scale synthetic LFE dataset containing 162 one-minute sequences using simulator, and capture a real-world testset using our custom-built sparse LFE acquisition system, covering diverse indoor and outdoor scenes. Extensive experiments demonstrate that S2D-LFE successfully generates up to 9x9 dense LFE from sparse 2x2 inputs and outperforms existing methods on both synthetic and real-world data. The datasets and code are available at https://github.com/Yutong2022/S2D-LFE.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2025/html/Liu_S2D-LFE_Sparse-to-Dense_Light_Field_Event_Generation_CVPR_2025_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2025/papers/Liu_S2D-LFE_Sparse-to-Dense_Light_Field_Event_Generation_CVPR_2025_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"s2d-lfe-sparse-to-dense-light-field-event","repo_url":"https://github.com/yutong2022/s2d-lfe","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"novel-view-synthesis","task_name":"Novel View Synthesis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}