Stitched Embeddings unifies 3D garment reconstruction and 2D pattern inference in a bidirectional latent space using BoxMesh as an intermediate representation.
In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition
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A multimodal pipeline decodes EEG into 3D meshes via EEG-to-image, MLLM reasoning, diffusion, and single-image-to-3D conversion, reporting 85.4% 10-way accuracy and 0.648 CLIPScore.
StreamEdit enables high-quality training-free video editing by adapting streaming video generation models with dual-branch fast sampling, self-attention bridge, cross-attention grounding, source-oriented guidance, and visual prompting, outperforming prior methods in few-step regimes.
A two-stage framework enables multimodal LLMs to learn shared latent representations from pairwise modality data and achieve cross-modal generation when incorporating new modalities.
LSRM scales transformer context windows with native sparse attention and geometric routing to deliver high-fidelity feed-forward 3D reconstruction and inverse rendering that approaches dense optimization quality.
citing papers explorer
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Stitched Embeddings: A Unified Latent Space for 3D Garments and 2D Patterns
Stitched Embeddings unifies 3D garment reconstruction and 2D pattern inference in a bidirectional latent space using BoxMesh as an intermediate representation.
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Brain3D: EEG-to-3D Decoding of Visual Representations via Multimodal Reasoning
A multimodal pipeline decodes EEG into 3D meshes via EEG-to-image, MLLM reasoning, diffusion, and single-image-to-3D conversion, reporting 85.4% 10-way accuracy and 0.648 CLIPScore.
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StreamEdit: Training-Free Video Editing via Few-Step Streaming Video Generation
StreamEdit enables high-quality training-free video editing by adapting streaming video generation models with dual-branch fast sampling, self-attention bridge, cross-attention grounding, source-oriented guidance, and visual prompting, outperforming prior methods in few-step regimes.
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Multimodal LLMs under Pairwise Modalities
A two-stage framework enables multimodal LLMs to learn shared latent representations from pairwise modality data and achieve cross-modal generation when incorporating new modalities.