Computer Vision

Easi3R: Training-free 4D reconstruction via attention disentanglement, enabling dynamic scene understanding from static 3D models.

TextCrafter: Precisely renders multiple texts in complex scenes, overcoming distortion and omission issues in existing visual text generation models.

MeshCraft: Efficient, controllable mesh generation using flow-based DiTs, outperforming auto-regressive methods in speed and user control.

New method for moving object segmentation by combining long-range motion cues, semantic features, and SAM2, achieving state-of-the-art performance in challenging scenarios.

ORIGEN: First zero-shot 3D orientation grounding in text-to-image generation.

Hi3DGen: High-fidelity 3D geometry generation from images via normal bridging.

X2-Gaussian enables continuous-time 4D CT reconstruction via dynamic radiative Gaussian splatting and self-supervised respiratory motion learning.

VBench 2.0: A new benchmark suite advancing video generation evaluation with intrinsic faithfulness metrics.

SparseFlex: Achieves high-res, arbitrary-topology 3D shape modeling via sparse isosurface representation and sectional voxel training. Revolutionizing 3D generative AI!

HSMR: Reconstructing 3D humans with a biomechanically accurate skeleton model from a single image, enhancing pose realism.

Adapting Stable Diffusion for faster Text-to-Mesh Generation, PRD efficiently creates high-quality 3D models without needing extensive 3D training data.

Optimal Stepsize Distillation accelerates diffusion sampling by distilling knowledge from reference trajectories, achieving 10x speedup with minimal performance loss.

Lumina-Image 2.0: A unified & efficient image generative framework, outperforming previous models with only 2.6B parameters.

LOCATEdit refines cross-attention maps with graph Laplacian regularization, achieving precise & localized text-guided image editing without artifacts.

LeX-Art: High-quality text-to-image generation via scalable data synthesis.

This survey explores the evolution of physics cognition in video generation, addressing the gap between visual realism and physical accuracy.

ChatAnyone: Stylized real-time portrait video generation with hierarchical motion diffusion model.

Fixing fine-tuned diffusion models! By using richer, unconditional priors, they generate better images and videos.

Synthetic data can enhance the physical realism of video synthesis, paving the way for more believable generated content.

Free4D: Tuning-free 4D scene generation with spatial-temporal consistency.