The paper presents a multimodal framework, dataset, and reconstruction pipeline to create immersive volumetric videos supporting large 6-DoF audiovisual interaction from real multi-view captures.
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GEAR is an EM-style alternating optimization framework that jointly models geometry and motion in Gaussian Splatting to improve reconstruction of complex articulated objects.
PD-4DGS decomposes 4DGS into static scaffold, global deformation, and local refinement layers using hierarchical decomposition and custom losses, achieving over 60% bitstream reduction and reducing first-frame latency to about 1.7 seconds on 2 Mbps links.
WARPED synthesizes realistic wrist-view observations from monocular egocentric human videos via foundation models, hand-object tracking, retargeting, and Gaussian Splatting to train visuomotor policies that match teleoperation success rates on five tabletop tasks with 5-8x less collection effort.
LIVE-GS uses an LLM to predict physical parameters from static Gaussian assets in 10 seconds for physics-aware VR interactions, validated by interviews, baseline comparisons, and user studies.
A framework is introduced to systematically assess the reconstruction fidelity of 3D Gaussian Splatting for vehicles and pedestrians in autonomous driving scenes from novel lateral and longitudinal viewpoints.
citing papers explorer
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Realizing Immersive Volumetric Video: A Multimodal Framework for 6-DoF VR Engagement
The paper presents a multimodal framework, dataset, and reconstruction pipeline to create immersive volumetric videos supporting large 6-DoF audiovisual interaction from real multi-view captures.
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GEAR: GEometry-motion Alternating Refinement for Articulated Object Modeling with Gaussian Splatting
GEAR is an EM-style alternating optimization framework that jointly models geometry and motion in Gaussian Splatting to improve reconstruction of complex articulated objects.
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PD-4DGS:Progressive Decomposition of 4D Gaussian Splatting for Bandwidth-Adaptive Dynamic Scene Streaming
PD-4DGS decomposes 4DGS into static scaffold, global deformation, and local refinement layers using hierarchical decomposition and custom losses, achieving over 60% bitstream reduction and reducing first-frame latency to about 1.7 seconds on 2 Mbps links.
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WARPED: Wrist-Aligned Rendering for Robot Policy Learning from Egocentric Human Demonstrations
WARPED synthesizes realistic wrist-view observations from monocular egocentric human videos via foundation models, hand-object tracking, retargeting, and Gaussian Splatting to train visuomotor policies that match teleoperation success rates on five tabletop tasks with 5-8x less collection effort.
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LIVE-GS: LLM Powers Interactive VR Experience with Physics-Aware Gaussian Splatting
LIVE-GS uses an LLM to predict physical parameters from static Gaussian assets in 10 seconds for physics-aware VR interactions, validated by interviews, baseline comparisons, and user studies.
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From Concept to Capability: Evaluating 3D Gaussian Splatting for Synthetic Scene Editing in Autonomous Driving
A framework is introduced to systematically assess the reconstruction fidelity of 3D Gaussian Splatting for vehicles and pedestrians in autonomous driving scenes from novel lateral and longitudinal viewpoints.