Miles Yang

Achieving a 0.499 face similarity score, WithEveryone revolutionizes group image generation by ensuring identity preservation for up to ten individuals without direct face copying.

Rosetta achieves multimodal integration without catastrophic forgetting, enabling models to expand their capabilities while retaining prior knowledge.

GEAR accelerates image synthesis convergence by up to 10x while enhancing feature coherence, challenging the traditional decoupling of tokenizers and generators.

Frame-level causal attention is all you need for effective visual reconstruction in unified multimodal models.

Current audio-visual models nail unimodal quality but still struggle to make music and dance move together rhythmically, highlighting a key gap TMD-Bench is designed to address.

LMMs can learn to generate images *and* improve their understanding abilities, without catastrophic forgetting, by carefully disentangling and sharing experts within a MoE architecture.

Achieve SOTA in both visual generation and understanding by harmonizing generative and semantic representations within a single ViT architecture.

Ditch discrete visual tokens: UniCom achieves SOTA multimodal generation by compressing continuous semantic representations, unlocking better controllability and consistency in image editing.

The largest open-source image generative model to date, HunyuanImage 3.0, achieves state-of-the-art performance using a Mixture-of-Experts architecture and native Chain-of-Thoughts schema.