🔥 Geometric Action Model for Robot Policy Learning

💡 The paper proposes a Geometric Action Model for robot policy learning that leverages pretrained geometric foundation models to enable language-conditioned manipulation policies in 3D physical environments. The problem addressed is that current vision-language-action models and video world-action models operate primarily on 2D image frames or 2D-derived latent spaces, leaving implicit the 3D geometry required for contact-rich manipulation.

The proposed method, Geometric Action Model, repurposes a pretrained geometric foundation model as a shared substrate for perception, temporal prediction, and action decoding. It splits the model at an intermediate layer, using the shallow layers as an observation encoder and inserting a causal future predictor to forecast future latent tokens conditioned on language, proprioception, and action history. The predicted future tokens are then routed through the remaining model blocks for feature propagation and decoding, allowing a single backbone to produce both future geometry and actions.

The results show that the Geometric Action Model is more accurate, more robust, faster, and lighter than current foundation-model-scale baselines across a broad suite of simulation and real-robot manipulation benchmarks. This design equips the geometric foundation model with language-conditioned temporal world modeling through minimal architectural modification while preserving its rich geometric priors, making it a significant contribution to robot policy learning in 3D physical environments.

📅 Published on Jun 15

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.17046
• PDF: https://arxiv.org/pdf/2606.17046
• Project Page: https://cvlab-kaist.github.io/Geometric-Action-Model/

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📢 By: https://xn--r1a.website/PaperNexus

#GeometricDeepLearning #RobotPolicyLearning #LanguageConditionedManipulation #3DPhysicalEnvironmentModeling #GeometricFoundationModels

🔥 MeshFlow: Mesh Generation with Equivariant Flow Matching

💡 MeshFlow is a method for generating triangle meshes directly using equivariant optimal-transport flow matching models. The problem of generating meshes is challenging due to the symmetries present in the representation, including permutation invariance of faces and vertices. Traditional autoregressive methods serialize meshes into long sequences, which can be slow and inefficient.

MeshFlow addresses this problem by learning to generate triangle meshes as triangle soups, which are unordered collections of triangles. The method uses equivariant optimal-transport flow matching models that respect the symmetries of triangle soups, including arbitrary permutations of faces and permutations of vertices within each face.

To achieve this, the authors propose a modification to the Diffusion Transformer architecture, resulting in a scalable network that can model a velocity field while maintaining the desired equivariance. The authors also introduce an optimal-transport-based training objective that improves convergence by eliminating supervision signals that violate these symmetries.

The results show that MeshFlow achieves mesh quality comparable to state-of-the-art autoregressive mesh generators, but provides a significant speedup of about 18 times during inference. This makes MeshFlow a more efficient and effective method for generating high-quality triangle meshes. Overall, the contributions of MeshFlow include a novel method for generating triangle meshes, a scalable and equivariant network architecture, and an optimal-transport-based training objective that improves convergence and mesh quality.

📅 Published on Jun 22

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.23489
• PDF: https://arxiv.org/pdf/2606.23489
• Project Page: https://qiisun.github.io/MeshFlow/

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📢 By: https://xn--r1a.website/PaperNexus

#EquivariantFlowMatching #MeshGeneration #OptimalTransportModels #TriangleMeshes #GeometricDeepLearning

🔥 Geometric Context Transformer for Streaming 3D Reconstruction

💡 The paper presents a new approach to streaming 3D reconstruction, which involves recovering 3D information such as camera poses and point clouds from a video stream. This task requires geometric accuracy, temporal consistency, and computational efficiency. To address this problem, the authors introduce LingBot-Map, a feed-forward 3D foundation model that uses a geometric context transformer architecture. The key component of this architecture is a specialized attention mechanism that integrates three main elements: an anchor context, a pose-reference window, and a trajectory memory. These elements work together to address coordinate grounding, dense geometric cues, and long-range drift correction, allowing the model to maintain a compact streaming state while retaining rich geometric context. The result is a model that can perform stable and efficient inference at around 20 frames per second on high-resolution inputs, even over long sequences exceeding 10,000 frames. The authors evaluate their approach on various benchmarks and demonstrate that it outperforms both existing streaming and iterative optimization-based methods, achieving superior performance in terms of geometric accuracy and temporal consistency. Overall, the paper contributes a novel and effective approach to streaming 3D reconstruction, with potential applications in areas such as robotics, computer vision, and virtual reality.

📅 Published on Apr 15

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2604.14141
• PDF: https://arxiv.org/pdf/2604.14141
• Project Page: https://technology.robbyant.com/lingbot-map

🤖 Models citing this paper:
• https://huggingface.co/robbyant/lingbot-map
• https://huggingface.co/agramoi/lingbot-map
• https://huggingface.co/maujim/lingbot-map-long-only

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/limonsyrah/lingbot-3d
• https://huggingface.co/spaces/mohan007/lingbot-3d
• https://huggingface.co/spaces/Fifthoply/lingbot-3d-ZERO

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📢 By: https://xn--r1a.website/PaperNexus

#GeometricDeepLearning #3DReconstructionAlgorithms #StreamingComputerVision #TransformerArchitectures #PointCloudProcessing

🔥 Native and Compact Structured Latents for 3D Generation

💡 This paper addresses the challenge of 3D generative modeling where existing representations struggle to capture complex topologies and detailed appearance of 3D assets. To overcome this, the authors introduce a new sparse voxel representation called O-Voxel, which encodes both geometry and appearance of 3D objects. O-Voxel can robustly model arbitrary topology, including open, non-manifold, and fully-enclosed surfaces, and captures comprehensive surface attributes. The authors design a Sparse Compression VAE based on O-Voxel, which provides a high spatial compression rate and a compact latent space. They train large-scale models with 4B parameters on diverse public 3D asset datasets and achieve highly efficient inference. The results show that the generated assets have significantly better geometry and material quality compared to existing models. The approach offers a significant advancement in 3D generative modeling by enabling high-quality generation with efficient inference and robust topology handling.

📅 Published on Dec 16, 2025

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2512.14692
• PDF: https://arxiv.org/pdf/2512.14692
• Project Page: https://microsoft.github.io/TRELLIS.2/

🤖 Models citing this paper:
• https://huggingface.co/microsoft/TRELLIS.2-4B
• https://huggingface.co/mancub/TRELLIS.2-4B
• https://huggingface.co/Jinstudio/TRELLIS.2-4B

📊 Datasets citing this paper:
• https://huggingface.co/datasets/serpentine-b/t2

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/microsoft/TRELLIS.2
• https://huggingface.co/spaces/TencentARC/Pixal3D
• https://huggingface.co/spaces/broyang/3dai

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📢 By: https://xn--r1a.website/PaperNexus

#3DGenerativeModeling #SparseVoxelRepresentation #CompactLatentSpace #3DAssetGeneration #GeometricDeepLearning