Pith. sign in

REVIEW 2 cited by

Layered Neural Rendering for Retiming People in Video

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2009.07833 v2 pith:6LOBMFAT submitted 2020-09-16 cs.CV cs.GR

Erika Lu , Forrester Cole , Tali Dekel , Weidi Xie , Andrew Zisserman , David Salesin , William T. Freeman , Michael Rubinstein This is my paper · ORCID
classification cs.CVcs.GR
keywords videopeopledifferentmotionsretimingachieveactionseffects
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

We present a method for retiming people in an ordinary, natural video -- manipulating and editing the time in which different motions of individuals in the video occur. We can temporally align different motions, change the speed of certain actions (speeding up/slowing down, or entirely "freezing" people), or "erase" selected people from the video altogether. We achieve these effects computationally via a dedicated learning-based layered video representation, where each frame in the video is decomposed into separate RGBA layers, representing the appearance of different people in the video. A key property of our model is that it not only disentangles the direct motions of each person in the input video, but also correlates each person automatically with the scene changes they generate -- e.g., shadows, reflections, and motion of loose clothing. The layers can be individually retimed and recombined into a new video, allowing us to achieve realistic, high-quality renderings of retiming effects for real-world videos depicting complex actions and involving multiple individuals, including dancing, trampoline jumping, or group running.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Surface-SOS: Self-Supervised Object Segmentation via Neural Surface Representation

    cs.CV 2025-01 conditional novelty 6.0 of 10

    A neural surface representation with foreground and background modules performs self-supervised object segmentation from multi-view images, producing finer masks than NeRF-based counterparts.

  2. Video Decomposition Prior: A Methodology to Decompose Videos into Layers

    cs.CV 2024-12 conditional novelty 5.0 of 10

    VDP decomposes a single test video into layers and opacity maps via two U-Nets, achieving strong unsupervised video object segmentation, dehazing, and relighting, though the relighting model reduces to gamma correction.

Pith tools