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Found 8 papers, 2 papers with code
HumanAesExpert: Advancing a Multi-Modality Foundation Model for Human Image Aesthetic Assessment
Image Aesthetic Assessment (IAA) is a long-standing and challenging research task.
FullDiT: Multi-Task Video Generative Foundation Model with Full Attention
Current video generative foundation models primarily focus on text-to-video tasks, providing limited control for fine-grained video content creation.
Improving Video Generation with Human Feedback
Video generation has achieved significant advances through rectified flow techniques, but issues like unsmooth motion and misalignment between videos and prompts persist.
ConceptMaster: Multi-Concept Video Customization on Diffusion Transformer Models Without Test-Time Tuning
To address these challenges, we introduce ConceptMaster, an innovative framework that effectively tackles the critical issues of identity decoupling while maintaining concept fidelity in customized videos.
NTIRE 2024 Quality Assessment of AI-Generated Content Challenge
A total of 196 participants have registered in the video track.
UNIAA: A Unified Multi-modal Image Aesthetic Assessment Baseline and Benchmark
To further evaluate the IAA capability of MLLMs, we construct the UNIAA-Bench, which consists of three aesthetic levels: Perception, Description, and Assessment.
Which Style Makes Me Attractive? Interpretable Control Discovery and Counterfactual Explanation on StyleGAN
First, we propose a novel approach to disentangle latent subspace semantics by exploiting existing face analysis models, e. g., face parsers and face landmark detectors.
SAFA: Structure Aware Face Animation
On the other hand, some area of the generated image might be occluded in the source image, which makes it difficult for GAN to generate realistic appearance.
