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Riza Alp Guler

9 accepted papers

2026

Omni-Attribute: Open-vocabulary Attribute Encoder for Visual Concept Personalization

Tsai-Shien Chen, Aliaksandr Siarohin, Gordon Guocheng Qian, Kuan-Chieh Jackson Wang, Egor Nemchinov, Moayed Haji-Ali +6
CVPR 2026

Visual concept personalization aims to transfer only specific image attributes, such as identity, expression, lighting, and style, into unseen contexts. However, existing methods rely on holistic embeddings from general-purpose image encoders, which entangle multiple visual factors and make it diffi

Cited by 0Source ↗code ↗Scholar ↗
2024

SPAD: Spatially Aware Multi-View Diffusers

Yash Kant, Aliaksandr Siarohin, Ziyi Wu, Michael Vasilkovsky, Guocheng Qian, Jian Ren +4
CVPR 2024poster

We present SPAD a novel approach for creating consistent multi-view images from text prompts or single images. To enable multi-view generation we repurpose a pretrained 2D diffusion model by extending its self-attention layers with cross-view interactions and fine-tune it on a high quality subset of…

Cited by 34Source ↗PDF ↗Scholar ↗
2023

Invertible Neural Skinning

Yash Kant, Aliaksandr Siarohin, Riza Alp Guler, Menglei Chai, Jian Ren, Sergey Tulyakov +1
CVPR 2023poster

Building animatable and editable models of clothed humans from raw 3D scans and poses is a challenging problem. Existing reposing methods suffer from the limited expressiveness of Linear Blend Skinning (LBS), require costly mesh extraction to generate each new pose, and typically do not preserve sur…

Cited by 8Source ↗PDF ↗code ↗Scholar ↗
2020

Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild

Dominik Kulon, Riza Alp Guler, Iasonas Kokkinos, Michael M. Bronstein, Stefanos Zafeiriou
CVPR 2020oral

We introduce a simple and effective network architecture for monocular 3D hand pose estimation consisting of an image encoder followed by a mesh convolutional decoder that is trained through a direct 3D hand mesh reconstruction loss. We train our network by gathering a large-scale dataset of hand ac…

Cited by 247PDF ↗Scholar ↗
2019

Slim DensePose: Thrifty Learning From Sparse Annotations and Motion Cues

Natalia Neverova, James Thewlis, Riza Alp Guler, Iasonas Kokkinos, Andrea Vedaldi
CVPR 2019oral

DensePose supersedes traditional landmark detectors by densely mapping image pixels to body surface coordinates. This power, however, comes at a greatly increased annotation cost, as supervising the model requires to manually label hundreds of points per pose instance. In this work, we thus seek met…

Cited by 40PDF ↗Scholar ↗
2018

Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance

Zhixin Shu, Mihir Sahasrabudhe, Riza Alp Guler, Dimitris Samaras, Nikos Paragios, Iasonas Kokkinos
ECCV 2018poster

In this work we introduce the Deforming Autoencoder, a generative model for images that disentangles shape from appearance in a latent representation space that is learned in a fully unsupervised manner. As in the deformable template paradigm, shape is represented as a diffeomorphism between a canon…

Cited by 249Source ↗PDF ↗Scholar ↗
2017

DenseReg: Fully Convolutional Dense Shape Regression In-The-Wild

Riza Alp Guler, George Trigeorgis, Epameinondas Antonakos, Patrick Snape, Stefanos Zafeiriou, Iasonas Kokkinos
CVPR 2017poster

In this paper we propose to learn a mapping from image pixels into a dense template grid through a fully convolutional network. We formulate this task as a regression problem and train our network by leveraging upon manually annotated facial landmarks 'in-the-wild'. We use such landmarks to establ…

Cited by 239PDF ↗Scholar ↗