Search Results for author:
Found 8 papers, 5 papers with code
Object pop-up: Can we infer 3D objects and their poses from human interactions alone?
The intimate entanglement between objects affordances and human poses is of large interest, among others, for behavioural sciences, cognitive psychology, and Computer Vision communities.
Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes
Indeed, we show that it is possible to train dense segmentation models using only bounding box labels.
3D Instance Segmentation
3D Semantic Instance Segmentation
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Interaction Replica: Tracking Human-Object Interaction and Scene Changes From Human Motion
In order for widespread adoption of such emerging applications, the sensor setup used to capture the interactions needs to be inexpensive and easy-to-use for non-expert users.
Stereo Radiance Fields (SRF): Learning View Synthesis for Sparse Views of Novel Scenes
Recent neural view synthesis methods have achieved impressive quality and realism, surpassing classical pipelines which rely on multi-view reconstruction.
Neural Unsigned Distance Fields for Implicit Function Learning
NDF represent surfaces at high resolutions as prior implicit models, but do not require closed surface data, and significantly broaden the class of representable shapes in the output.
SHARP 2020: The 1st Shape Recovery from Partial Textured 3D Scans Challenge Results
Additionally, two unique datasets of 3D scans are proposed, to provide raw ground-truth data for the benchmarks.
Implicit Feature Networks for Texture Completion from Partial 3D Data
Instead, we focus on 3D texture and geometry completion from partial and incomplete 3D scans.
Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion
To solve this, we propose Implicit Feature Networks (IF-Nets), which deliver continuous outputs, can handle multiple topologies, and complete shapes for missing or sparse input data retaining the nice properties of recent learned implicit functions, but critically they can also retain detail when it is present in the input data, and can reconstruct articulated humans.
