Computer Vision

KMM: Key Frame Mask Mamba generates extended, diverse human motion from text prompts by innovatively masking key frames in the Mamba architecture and using contrastive learning for improved text-motio…

StdGEN: Generate high-quality, semantically decomposed 3D characters from a single image in minutes, enabling flexible customization for various applications.

SVDQuant boosts 4-bit diffusion models by absorbing outliers via low-rank components, achieving 3.5x memory reduction and 3x speedup on 12B parameter models.

SG-I2V: Zero-shot controllable image-to-video generation using a self-guided approach that leverages pre-trained models for precise object and camera motion control.

ReCapture generates videos with novel camera angles from user videos using masked video fine-tuning, preserving scene motion and plausibly hallucinating unseen parts.

GazeGen uses real-time gaze tracking to enable intuitive hands-free visual content creation and editing, setting a new standard for accessible AR/VR interaction.

DimensionX generates photorealistic 3D and 4D scenes from a single image via controllable video diffusion, enabling precise manipulation of spatial structure and temporal dynamics.

GarVerseLOD introduces a novel dataset and framework for high-fidelity 3D garment reconstruction from a single image, achieving unprecedented robustness via a hierarchical approach and leveraging a ma…

Visual saliency boosts object detection accuracy more than depth estimation, especially for larger objects, offering valuable insights for model and dataset improvement.

Training-free Regional Prompting for FLUX boosts compositional text-to-image generation by cleverly manipulating attention mechanisms, achieving fine-grained control without retraining.

Scaling video generation models doesn’t guarantee they’ll learn physics; this study reveals they prioritize visual cues over true physical understanding.

GenXD: A unified model generating high-quality 3D & 4D scenes from any number of images, advancing the field of dynamic scene generation.

Adaptive Caching (AdaCache) dramatically speeds up video generation with diffusion transformers by cleverly caching and reusing computations, tailoring the process to each video’s complexity and motio…

DreamPolish: A new text-to-3D model generates highly detailed 3D objects with polished surfaces and realistic textures using progressive geometry refinement and a novel domain score distillation tech…

Randomized Autoregressive Modeling (RAR) sets a new state-of-the-art in image generation by cleverly introducing randomness during training to improve the model’s ability to learn from bidirectional c…

Constant Acceleration Flow (CAF) dramatically speeds up diffusion model generation by using a constant acceleration equation, outperforming state-of-the-art methods with improved accuracy and few-step…

High-performing video representation models can be trained using only synthetic videos and images, eliminating the need for large natural video datasets.

In-Context LoRA empowers existing text-to-image models for high-fidelity multi-image generation by simply concatenating images and using minimal task-specific LoRA tuning.

DELTA: A new method efficiently tracks every pixel in 3D space from monocular videos, enabling accurate motion estimation across entire videos with state-of-the-art accuracy and over 8x speed improvem…

HelloMeme enhances text-to-image models by integrating spatial knitting attentions, enabling high-fidelity meme video generation while preserving model generalization.