{"schema":"record/v1","id":"art_01M5MBXNZ3BXM77GJHH6RQ8J78","type":"article","revision":1,"etag":"\"r1.s0\"","title":"Can I get a free-viewpoint 4D scene from one ordinary (phone) video, and how do I tell whether a monocular dynamic-scene result is real?","summary":"The primary sources read say: not reliably. The DyCheck paper (arXiv 2210.13445v1) shows that common monocular benchmarks effectively leak multi-view signal through teleporting cameras or quasi-static scenes, and that with realistic camera motion and complex motion published NeRF-era methods lose roughly 1-2 dB (masked PSNR, no multi-view cues) and 4-5 dB (complex motion) per the abstract. The 4D-GS paper's own appendix reports overfitting to training views on monocular iPhone-style input and failure on large motion, and Dynamic 3D Gaussians states it needs a multi-camera rig. A reader can check a monocular claim by looking at how the camera moves relative to the scene (EMF), whether metrics are co-visibility masked, and whether sequences are not quasi-static. DyCheck evaluated NeRF-era models (T-NeRF, NSFF, Nerfies, HyperNeRF), not Gaussian methods; no read source reports a Gaussian method on its masked metrics.","status":"active","body":{"text":"The primary sources read say: not reliably. The DyCheck paper (arXiv 2210.13445v1) shows that common monocular benchmarks effectively leak multi-view signal through teleporting cameras or quasi-static scenes, and that with realistic camera motion and complex motion published NeRF-era methods lose roughly 1-2 dB (masked PSNR, no multi-view cues) and 4-5 dB (complex motion) per the abstract. The 4D-GS paper's own appendix reports overfitting to training views on monocular iPhone-style input and failure on large motion, and Dynamic 3D Gaussians states it needs a multi-camera rig. A reader can check a monocular claim by looking at how the camera moves relative to the scene (EMF), whether metrics are co-visibility masked, and whether sequences are not quasi-static. DyCheck evaluated NeRF-era models (T-NeRF, NSFF, Nerfies, HyperNeRF), not Gaussian methods; no read source reports a Gaussian method on its masked metrics.\n\nCurator qualifications: Excerpted evidence located (one excerpt-less row). Monocular free-viewpoint 4D is not reliable per DyCheck; D-NeRF 34.05 PSNR vs DyCheck 16-21 dB masked PSNR are different data/metrics, kept.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/html/2210.13445v1","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/html/2310.08528v3","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/html/2310.10642v3","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/html/2308.09713v1","note":"candidate evidence excerpts"},{"url":"https://github.com/KAIR-BAIR/dycheck","note":"candidate evidence excerpts"}]},"categories":["T11","T15","T19"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"source_inspected","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[{"url":"https://arxiv.org/html/2210.13445v1","claim":"The primary sources read say: not reliably. 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The DyCheck paper (arXiv 2210.13445v1) shows that common monocular benchmarks effectively leak multi-view signal through teleporting cameras or quasi-static scenes, and that with realistic camera motion and complex motion published NeRF-era methods lose roughly 1-2 dB (masked PSNR, no multi-view cues) and 4-5 dB (complex motion) per the abstract. The 4D-GS paper's own appendix reports overfitting to training views on monocular iPhone-style input and failure on large motion, and Dynamic 3D Gaussians states it needs a multi-camera rig. A reader can check a monocular claim by looking at how the camera moves relative to the scene (EMF), whether metrics are co-visibility masked, and whether sequences are not quasi-static. DyCheck evaluated NeRF-era models (T-NeRF, NSFF, Nerfies, HyperNeRF), not Gaussian methods; no read source reports a Gaussian method on its masked metrics.\n\nCurator qualifications: Excerpted evidence located (one excerpt-less row). Monocular free-viewpoint 4D is not reliable per DyCheck; D-NeRF 34.05 PSNR vs DyCheck 16-21 dB masked PSNR are different data/metrics, kept.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/html/2210.13445v1","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/html/2310.08528v3","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/html/2310.10642v3","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/html/2308.09713v1","note":"candidate evidence excerpts"},{"url":"https://github.com/KAIR-BAIR/dycheck","note":"candidate evidence excerpts"}]},"tags":[],"type":"article","links":[],"title":"Can I get a free-viewpoint 4D scene from one ordinary (phone) video, and how do I tell whether a monocular dynamic-scene result is real?","rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"summary":"The primary sources read say: not reliably. The DyCheck paper (arXiv 2210.13445v1) shows that common monocular benchmarks effectively leak multi-view signal through teleporting cameras or quasi-static scenes, and that with realistic camera motion and complex motion published NeRF-era methods lose roughly 1-2 dB (masked PSNR, no multi-view cues) and 4-5 dB (complex motion) per the abstract. The 4D-GS paper's own appendix reports overfitting to training views on monocular iPhone-style input and failure on large motion, and Dynamic 3D Gaussians states it needs a multi-camera rig. A reader can check a monocular claim by looking at how the camera moves relative to the scene (EMF), whether metrics are co-visibility masked, and whether sequences are not quasi-static. DyCheck evaluated NeRF-era models (T-NeRF, NSFF, Nerfies, HyperNeRF), not Gaussian methods; no read source reports a Gaussian method on its masked metrics.","evidence":{"claims":[{"url":"https://arxiv.org/html/2210.13445v1","claim":"The primary sources read say: not reliably. The DyCheck paper (arXiv 2210.13445v1) shows that common monocular benchmarks effectively leak multi-view signal through teleporting cameras or quasi-static scenes, and that with realistic camera motion and complex motion published NeRF-era methods lose roughly 1-2 dB (masked PSNR, no multi-view cues) and 4-5 dB (complex motion) per the abstract. The 4D-GS paper's own appendix reports overfitting to training views on monocular iPhone-style input and failure on large motion, and Dynamic 3D Gaussians states it needs a multi-camera rig. 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