7 papers
Scale-Wise VAR is Secretly Discrete Diffusion
Amandeep Kumar, Nithin Gopalakrishnan Nair, Vishal M. Patel
Autoregressive (AR) transformers have emerged as a powerful paradigm for visual generation, largely due to their scalability, computational efficiency and unified architecture with…
Scaling Transformer-Based Novel View Synthesis Models with Token Disentanglement and Synthetic Data
Nithin Gopalakrishnan Nair, Srinivas Kaza, Xuan Luo +3
Large transformer-based models have made significant progress in generalizable novel view synthesis (NVS) from sparse input views, generating novel viewpoints without the need for…
GenDeg: Diffusion-based Degradation Synthesis for Generalizable All-In-One Image Restoration
Sudarshan Rajagopalan, Nithin Gopalakrishnan Nair, Jay N. Paranjape +1
Deep learning-based models for All-In-One Image Restoration (AIOR) have achieved significant advancements in recent years. However, their practical applicability is limited by poor…
PETALface: Parameter Efficient Transfer Learning for Low-resolution Face Recognition
Kartik Narayan, Nithin Gopalakrishnan Nair, Jennifer Xu +2
Pre-training on large-scale datasets and utilizing margin-based loss functions have been highly successful in training models for high-resolution face recognition. However, these m…
Dreamguider: Improved Training free Diffusion-based Conditional Generation
Nithin Gopalakrishnan Nair, Vishal M Patel
Diffusion models have emerged as a formidable tool for training-free conditional generation.However, a key hurdle in inference-time guidance techniques is the need for compute-heav…
MaxFusion: Plug&Play Multi-Modal Generation in Text-to-Image Diffusion Models
Nithin Gopalakrishnan Nair, Jeya Maria Jose Valanarasu, Vishal M Patel
Large diffusion-based Text-to-Image (T2I) models have shown impressive generative powers for text-to-image generation as well as spatially conditioned image generation. For most ap…