Computer Vision

Researchers significantly enhanced autoregressive image generation by integrating chain-of-thought reasoning strategies, achieving a remarkable +24% improvement on the GenEval benchmark.

Video Depth Anything achieves consistent depth estimation for super-long videos by enhancing Depth Anything V2 with a spatial-temporal head and a novel temporal consistency loss, setting a new state-o…

TokenVerse: Extract & combine visual concepts from multiple images for creative image generation!

Hunyuan3D 2.0: A groundbreaking open-source system generating high-resolution, textured 3D assets using scalable diffusion models, exceeding state-of-the-art performance.

GPS-guided image generation is here! This paper leverages GPS data to create highly realistic images reflecting specific locations, even reconstructing 3D models from 2D photos.

EMO2 achieves realistic audio-driven avatar video generation by employing a two-stage framework: first generating hand poses directly from audio and then using a diffusion model to synthesize full-bod…

X-Dyna: a novel diffusion-based pipeline generates realistic human image animation using a zero-shot approach by integrating a Dynamics-Adapter for dynamic detail preservation, exceeding state-of-the-…

Textoon: Generating vivid 2D cartoon characters from text descriptions in under a minute, revolutionizing animation workflow.

GSTAR: A novel method achieving photorealistic rendering, accurate reconstruction, and reliable 3D tracking of dynamic scenes with changing topology, even handling surfaces appearing, disappearing, or…

GaussianAvatar-Editor enables photorealistic, text-driven editing of animatable 3D heads, solving motion occlusion and ensuring temporal consistency.

DiffuEraser: a novel video inpainting model based on stable diffusion, surpasses existing methods by using injected priors and temporal consistency improvements for superior results.

VideoWorld shows AI can learn complex reasoning and planning skills from unlabeled videos alone, achieving professional-level performance in Go and robotics.

SynthLight: A novel diffusion model relights portraits realistically by learning to re-render synthetic faces, generalizing remarkably well to real photographs.

Scaling visual tokenizers dramatically improves image and video generation, achieving state-of-the-art results and outperforming existing methods with fewer computations by focusing on decoder scaling…

Boosting diffusion model performance at inference time, this research introduces a novel framework that goes beyond simply increasing denoising steps. By cleverly searching for better noise candidates…

CaPa: Carve-n-Paint Synthesis generates hyper-realistic 4K textured meshes in under 30 seconds, setting a new standard for efficient 3D asset creation.

AnyStory: A unified framework enables high-fidelity personalized image generation for single and multiple subjects, addressing subject fidelity challenges in existing methods.

RepVideo enhances text-to-video generation by enriching feature representations, resulting in significantly improved temporal coherence and spatial detail.

Ouroboros-Diffusion: A novel tuning-free long video generation framework achieving unprecedented content consistency by cleverly integrating information across frames via latent sampling, cross-frame…

CityDreamer4D generates realistic, unbounded 4D city models by cleverly separating dynamic objects (like vehicles) from static elements (buildings, roads), using multiple neural fields for enhanced re…