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However, the volume rendering procedures that drive these representations necessitate careful trade-offs in terms of quality, rendering speed, and memory efficiency. In particular, existing methods fail to simultaneously achieve real-time performance, small memory footprint, and high-quality rendering for challenging real-world scenes. To address these issues, we present HyperReel -- a novel 6-DoF video representation. The two core components of HyperReel are: (1) a ray-conditioned sample prediction network that enables high-fidelity, high frame rate rendering at high resolutions and (2) a compact and memory-efficient dynamic volume representation. Our 6-DoF video pipeline achieves the best performance compared to prior and contemporary approaches in terms of visual quality with small memory requirements, while also rendering at up to 18 frames-per-second at megapixel resolution without any custom CUDA code.","url_abs":"https://arxiv.org/abs/2301.02238v2","url_pdf":"https://arxiv.org/pdf/2301.02238v2.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":"hyperreel-high-fidelity-6-dof-video-with-ray","repo_url":"https://github.com/facebookresearch/hyperreel","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"novel-view-synthesis","task_name":"Novel View Synthesis"},{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"}],"methods":[{"method_slug":"fail","method_name":"fail"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/novel-view-synthesis-on-donerf-evaluation","task":"Novel View Synthesis","dataset":"DONeRF: Evaluation Dataset","model":"HyperReel","rank_in_archive_order":1,"of":6,"metrics":{"PSNR":"35.1"},"uses_additional_data":false},{"leaderboard":"/sota/novel-view-synthesis-on-donerf-evaluation","task":"Novel View Synthesis","dataset":"DONeRF: Evaluation Dataset","model":"Instant NGP","rank_in_archive_order":2,"of":6,"metrics":{"PSNR":"33.1"},"uses_additional_data":false},{"leaderboard":"/sota/novel-view-synthesis-on-donerf-evaluation","task":"Novel View Synthesis","dataset":"DONeRF: Evaluation Dataset","model":"NeRF","rank_in_archive_order":3,"of":6,"metrics":{"PSNR":"30.9"},"uses_additional_data":false},{"leaderboard":"/sota/novel-view-synthesis-on-donerf-evaluation","task":"Novel View Synthesis","dataset":"DONeRF: Evaluation Dataset","model":"AdaNeRF","rank_in_archive_order":4,"of":6,"metrics":{"PSNR":"30.9"},"uses_additional_data":false},{"leaderboard":"/sota/novel-view-synthesis-on-donerf-evaluation","task":"Novel View Synthesis","dataset":"DONeRF: Evaluation Dataset","model":"DoNeRF","rank_in_archive_order":5,"of":6,"metrics":{"PSNR":"30.8"},"uses_additional_data":false},{"leaderboard":"/sota/novel-view-synthesis-on-donerf-evaluation","task":"Novel View Synthesis","dataset":"DONeRF: Evaluation Dataset","model":"TermiNeRF","rank_in_archive_order":6,"of":6,"metrics":{"PSNR":"29.8"},"uses_additional_data":false},{"leaderboard":"/sota/novel-view-synthesis-on-llff","task":"Novel View Synthesis","dataset":"LLFF","model":"HyperReel","rank_in_archive_order":9,"of":15,"metrics":{"PSNR":"26.2"},"uses_additional_data":false},{"leaderboard":"/sota/novel-view-synthesis-on-llff","task":"Novel View Synthesis","dataset":"LLFF","model":"AdaNeRF","rank_in_archive_order":11,"of":15,"metrics":{"PSNR":"25.7"},"uses_additional_data":false},{"leaderboard":"/sota/novel-view-synthesis-on-llff","task":"Novel View Synthesis","dataset":"LLFF","model":"Instant NGP","rank_in_archive_order":13,"of":15,"metrics":{"PSNR":"25.6"},"uses_additional_data":false},{"leaderboard":"/sota/novel-view-synthesis-on-llff","task":"Novel View Synthesis","dataset":"LLFF","model":"TermiNeRF","rank_in_archive_order":14,"of":15,"metrics":{"PSNR":"23.6"},"uses_additional_data":false},{"leaderboard":"/sota/novel-view-synthesis-on-llff","task":"Novel View Synthesis","dataset":"LLFF","model":"DoNeRF","rank_in_archive_order":15,"of":15,"metrics":{"PSNR":"22.9"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2301.02238","atlas_url":"https://app.syntology.ai/?focus=2301.02238","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.02238"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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