Trends · 研究趋势
数据来自 Hugging Face 论文的 AI 提取关键词,按月统计研究方向的增长与热度。
Chuyan Chen, Haoxing Chen, Kun Chen +27 authors
LLaDA-Image unifies a 6B diffusion transformer with a frozen vision-language module, using image-only pre-training and a Muon optimizer to generate photorealistic images with precise editing, and is distilled into a fast 2-4 step variant that achieves state-of-the-art open-source results.
Ziyang Ma, Zhikang Niu, Wenming Tu +30 authors
AuK is an open-source foundational model that unifies speech generation and editing via natural-language instructions and audio context, using a multimodal language model, joint VAE, hybrid rectified-flow Transformer, and efficient distillation for fast inference.
Jingfeng Yao, Yuda Song, Yucong Zhou +1 authors
A unified visual tokenizer pre-training framework (VTP) improves generative performance by optimizing image-text contrastive, self-supervised, and reconstruction losses, leading to better scaling properties and higher zero-shot accuracy and faster convergence.
Hangrui Hu, Xinfa Zhu, Ting He +13 authors
The Qwen3-TTS series presents advanced multilingual text-to-speech models with voice cloning and controllable speech generation capabilities, utilizing dual-track LM architecture and specialized speech tokenizers for efficient streaming synthesis.
Yudong Jin, Tao Xie, Qihang Zhang +6 authors
4DAnyone reconstructs 4D humans from monocular video by generating multiview-consistent videos and lifting them into 4D Gaussian Splatting, using reference and target context designs to overcome scaling bottlenecks.
Zongyu Lin, Wei Liu, Chen Chen +14 authors
STIV, a text-image-conditioned video generation method integrating Diffusion Transformer and classifier-free guidance, achieves state-of-the-art performance in text-to-video, text-image-to-video, and image-to-video tasks.
Peize Sun, Yi Jiang, Shoufa Chen +4 authors
LlamaGen applies the next-token prediction paradigm from large language models to image generation, achieving state-of-the-art performance with various model sizes and conditions.
Zhenxiong Tan, Songhua Liu, Xingyi Yang +2 authors
OminiControl, a parameter-efficient framework, integrates image conditions into Diffusion Transformer models using a parameter reuse mechanism, excelling in subject-driven and spatially-aligned conditional generation.
Guoqing Ma, Haoyang Huang, Kun Yan +112 authors
Step-Video-T2V is a state-of-the-art text-to-video model with 30B parameters, utilizing a deep compression Variational Autoencoder and a DiT with 3D full attention for high-quality video generation.
Igor Pavlovic, Thiemo Wandel, Anton Obukhov +6 authors
Marigold V2 repurposes diffusion transformers for monocular depth estimation via single-step flow-matching inference, semantic alignment, and a Sinkhorn-based two-stage fine-tuning protocol, yielding sharper out-of-distribution depth maps and strong results on related dense regression tasks.
Junsong Chen, Chongjian Ge, Enze Xie +7 authors
PixArt-\Sigma, a Diffusion Transformer model, generates high-quality 4K images more efficiently than existing models using improved data and novel token compression techniques.
Liangyang Ouyang, Ruicong Liu, Xuangeng Chu +2 authors
HelloWorld is a video world model that enables users to trigger natural social responses from on-screen characters via self-distilled training and inference-time cross-attention modulation.
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