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D3D-HOI: Dynamic 3D Human-Object Interactions from Videos

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arxiv 2108.08420 v1 pith:4DW35IPP submitted 2021-08-19 cs.CV

Xiang Xu , Hanbyul Joo , Greg Mori , Manolis Savva This is my paper
classification cs.CV
keywords datasetobjectarticulatedd3d-hoihuman-objectvideoschallengingevaluate
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We introduce D3D-HOI: a dataset of monocular videos with ground truth annotations of 3D object pose, shape and part motion during human-object interactions. Our dataset consists of several common articulated objects captured from diverse real-world scenes and camera viewpoints. Each manipulated object (e.g., microwave oven) is represented with a matching 3D parametric model. This data allows us to evaluate the reconstruction quality of articulated objects and establish a benchmark for this challenging task. In particular, we leverage the estimated 3D human pose for more accurate inference of the object spatial layout and dynamics. We evaluate this approach on our dataset, demonstrating that human-object relations can significantly reduce the ambiguity of articulated object reconstructions from challenging real-world videos. Code and dataset are available at https://github.com/facebookresearch/d3d-hoi.

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Cited by 6 Pith papers

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  2. OmniShow: Unifying Multimodal Conditions for Human-Object Interaction Video Generation

    cs.CV 2026-04 unverdicted novelty 6.0 of 10

    OmniShow unifies text, image, audio, and pose conditions into an end-to-end model for high-quality human-object interaction video generation and introduces the HOIVG-Bench benchmark, claiming state-of-the-art results.

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