MVCHead uses a hierarchical state space model with bi-directional scans and an SE(3) critic to enforce 3D consistency in Gaussian avatars trained only on 2D images.
Imagenet: A large-scale hierarchical image database
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SFHand presents the first streaming language-guided autoregressive framework for 3D hand forecasting, achieving up to 35.8% gains over prior methods and 13.4% better downstream embodied task performance.
VibeToken enables autoregressive image generation at arbitrary resolutions using 64 tokens for 1024x1024 images with 3.94 gFID, constant 179G FLOPs, and better efficiency than diffusion or fixed AR baselines.
Pretrained vision transformers use specific attention heads sensitive to Gestalt continuity for object binding, shown via probes on synthetic datasets and ablation experiments.
Lite Any Stereo delivers top-ranked zero-shot accuracy on four real-world stereo benchmarks using a lightweight backbone, hybrid cost aggregation, and three-stage training on million-scale data, at less than 1% of typical computational cost.
citing papers explorer
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Multi-view Consistent 3D Gaussian Head Avatars 'without' Multi-view Generation
MVCHead uses a hierarchical state space model with bi-directional scans and an SE(3) critic to enforce 3D consistency in Gaussian avatars trained only on 2D images.
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SFHand: Learning Embodied Manipulation by Streaming Egocentric 3D Hand Forecasting
SFHand presents the first streaming language-guided autoregressive framework for 3D hand forecasting, achieving up to 35.8% gains over prior methods and 13.4% better downstream embodied task performance.
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VibeToken: Scaling 1D Image Tokenizers and Autoregressive Models for Dynamic Resolution Generations
VibeToken enables autoregressive image generation at arbitrary resolutions using 64 tokens for 1024x1024 images with 3.94 gFID, constant 179G FLOPs, and better efficiency than diffusion or fixed AR baselines.
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I Walk the Line: Examining the Role of Gestalt Continuity in Object Binding for Vision Transformers
Pretrained vision transformers use specific attention heads sensitive to Gestalt continuity for object binding, shown via probes on synthetic datasets and ablation experiments.
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Lite Any Stereo: Efficient Zero-Shot Stereo Matching
Lite Any Stereo delivers top-ranked zero-shot accuracy on four real-world stereo benchmarks using a lightweight backbone, hybrid cost aggregation, and three-stage training on million-scale data, at less than 1% of typical computational cost.