1 citations · 1 across the 7 of their papers we have counts for
7 papers
Advanced Pixel Diffusion Model with Guided Sparse Global Refinement
Weiyi You, Jinhua Zhang, Xingyu Zhou +3
Pixel-space diffusion has recently emerged as a promising direction for high-fidelity image generation by modeling images directly in the original pixel domain. However, pixel-spac…
Beyond Token-Level Cross-Entropy: Fréchet Distributional Post-Training for Autoregressive Image Generation
Jinhua Zhang, Yisong Lin, Wei Long +1
Autoregressive image generators are commonly pretrained with token-level cross-entropy under teacher forcing, yet evaluated by the distributional quality of decoded images. This cr…
Taming Sampling Perturbations with Variance Expansion Loss for Latent Diffusion Models
Qifan Li, Xingyu Zhou, Jinhua Zhang +2
Latent diffusion models have emerged as the dominant framework for high-fidelity and efficient image generation, owing to their ability to learn diffusion processes in compact late…
IDESplat: Iterative Depth Probability Estimation for Generalizable 3D Gaussian Splatting
Wei Long, Haifeng Wu, Shiyin Jiang +3
Generalizable 3D Gaussian Splatting aims to directly predict Gaussian parameters using a feed-forward network for scene reconstruction. Among these parameters, Gaussian means are p…
Texture Vector-Quantization and Reconstruction Aware Prediction for Generative Super-Resolution
Qifan Li, Jiale Zou, Jinhua Zhang +3
Vector-quantized based models have recently demonstrated strong potential for visual prior modeling. However, existing VQ-based methods simply encode visual features with nearest c…
Generative Image Compression by Estimating Gradients of the Rate-variable Feature Distribution
Minghao Han, Weiyi You, Jinhua Zhang +3
While learned image compression (LIC) focuses on efficient data transmission, generative image compression (GIC) extends this framework by integrating generative modeling to produc…
MVAR: Visual Autoregressive Modeling with Scale and Spatial Markovian Conditioning
Jinhua Zhang, Wei Long, Minghao Han +2
Essential to visual generation is efficient modeling of visual data priors. Conventional next-token prediction methods define the process as learning the conditional probability di…