Image Generation

Omegance: One parameter precisely controls image detail in diffusion models, enabling flexible granularity adjustments without model changes or retraining.

ConsisID achieves high-quality, identity-preserving text-to-video generation using a tuning-free diffusion transformer model that leverages frequency decomposition for effective identity control.

DreamMix enhances image inpainting by disentangling object attributes for precise editing, enabling both identity preservation and flexible text-driven modifications.

DreamCache enables efficient, high-quality personalized image generation without finetuning by caching reference image features and using lightweight conditioning adapters.

Collaborative Decoding (CoDe) dramatically boosts visual auto-regressive model efficiency.

ChatGen-Evo automates text-to-image generation from freestyle chatting, simplifying the process and significantly improving performance over existing methods.

AnchorCrafter animates cyber-anchors selling products via human-object interacting video generation, achieving high visual fidelity and controllable interactions.

Image editing is revolutionized by Frame2Frame, which uses video generation to produce seamless and accurate edits, preserving image fidelity.

OneDiffusion: A single diffusion model masters image synthesis & understanding across diverse tasks, from text-to-image to depth estimation, pushing the boundaries of AI.

FQGAN revitalizes image generation by introducing Factorized Quantization, enabling scalable and stable visual tokenization with state-of-the-art performance.

BootComp: generate realistic human images wearing multiple garments using a novel synthetic data pipeline & diffusion model, enabling diverse applications like virtual try-on.

New benchmark VCT² reveals limitations of AI-generated image detectors; Visual AI Index (VAI) provides a robust evaluation framework.

Diptych Prompting: a novel zero-shot subject-driven image generator leveraging large-scale text-to-image models and inpainting for precise subject alignment and high-quality image synthesis.

TEXGen: A groundbreaking generative diffusion model creates high-resolution 3D mesh textures directly from text and image prompts, exceeding prior methods in quality and efficiency.

Style-friendly SNR sampler biases diffusion model training towards higher noise levels, enabling it to learn and generate images with higher style fidelity.

OminiControl: A minimal, universal framework efficiently integrates image conditions into diffusion transformers, enabling diverse and precise control over image generation.

Morph: a novel motion-free physics optimization framework drastically enhances human motion generation’s physical plausibility using synthetic data, achieving state-of-the-art quality.

Stable Flow achieves diverse, consistent image editing without training by strategically injecting source image features into specific ‘vital’ layers of a diffusion transformer model. This training-f…

MyTimeMachine personalizes facial age transformation using just 50 personal photos, outperforming existing methods by generating re-aged faces that closely match a person’s actual appearance at variou…

VBench++: A new benchmark suite meticulously evaluates video generative models across 16 diverse dimensions, aligning with human perception for improved model development and fairer comparisons.