Free-Range Gaussians uses flow matching over Gaussian parameters to predict non-grid-aligned 3D Gaussians from multi-view images, enabling synthesis of plausible content in unobserved regions with fewer primitives than grid-aligned methods.
In: ICLR (2023)
4 Pith papers cite this work. Polarity classification is still indexing.
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An inference-time optimization using a control-energy objective on pretrained diffusion models enables coherent long-range human motion generation with explicit domain transitions.
FASTER adds a Horizon-Aware Schedule to flow VLAs that compresses immediate-action denoising to one step while keeping long-horizon trajectory quality, lowering real-robot reaction latency.
MV-SAM3D adds multi-view fusion via multi-diffusion with attention-entropy and visibility weighting plus physics-aware optimization to improve fidelity and physical plausibility in layout-aware 3D generation.
citing papers explorer
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Free-Range Gaussians: Non-Grid-Aligned Generative 3D Gaussian Reconstruction
Free-Range Gaussians uses flow matching over Gaussian parameters to predict non-grid-aligned 3D Gaussians from multi-view images, enabling synthesis of plausible content in unobserved regions with fewer primitives than grid-aligned methods.
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Diffusion Path Alignment for Long-Range Motion Generation and Domain Transitions
An inference-time optimization using a control-energy objective on pretrained diffusion models enables coherent long-range human motion generation with explicit domain transitions.
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FASTER: Rethinking Real-Time Flow VLAs
FASTER adds a Horizon-Aware Schedule to flow VLAs that compresses immediate-action denoising to one step while keeping long-horizon trajectory quality, lowering real-robot reaction latency.
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MV-SAM3D: Adaptive Multi-View Fusion for Layout-Aware 3D Generation
MV-SAM3D adds multi-view fusion via multi-diffusion with attention-entropy and visibility weighting plus physics-aware optimization to improve fidelity and physical plausibility in layout-aware 3D generation.