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Babak Taati

6 accepted papers

2026

Face2Scene: Using Facial Degradation as an Oracle for Diffusion-Based Scene Restoration

Amirhossein Kazerouni, Maitreya Suin, Tristan Aumentado-Armstrong, Sina Honari, Amanpreet Walia, Iqbal Mohomed +3
CVPR 2026

Recent advances in image restoration have enabled high-fidelity recovery of faces from degraded inputs using reference-based face restoration models (Ref-FR). However, such methods focus solely on facial regions, neglecting degradation across the full scene, including body and background, which limi

Cited by 0Source ↗Scholar ↗
2026

LoopFormer: Elastic-Depth Looped Transformers for Latent Reasoning via Shortcut Modulation

Ahmadreza Jeddi, Marco Ciccone, Babak Taati
ICLR 2026poster

Looped Transformers have emerged as an efficient and powerful class of models for reasoning in the language domain. Recent studies show that these models achieve strong performance on algorithmic and reasoning tasks, suggesting that looped architectures possess an inductive bias toward latent reason…

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2025

Care-PD: A Multi-Site Anonymized Clinical Dataset for Parkinson’s Disease Gait Assessment

Vida Adeli, Ivan Klabučar, Javad Rajabi, Benjamin Filtjens, Soroush Mehraban, Diwei Wang +22
NeurIPS 2025poster

Objective gait assessment in Parkinson’s Disease (PD) is limited by the absence of large, diverse, and clinically annotated motion datasets. We introduce Care-PD, the largest publicly available archive of 3D mesh gait data for PD, and the first multi-site collection spanning 9 cohorts from 8 clinica…

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2025

LIFT: Latent Implicit Functions for Task- and Data-Agnostic Encoding

Amirhossein Kazerouni, Soroush Mehraban, Michael Brudno, Babak Taati
ICCV 2025poster

Implicit Neural Representations (INRs) are proving to be a powerful paradigm in unifying task modeling across diverse data domains, offering key advantages such as memory efficiency and resolution independence. Conventional deep learning models are typically modality-dependent, often requiring custo…

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2025

Token Perturbation Guidance for Diffusion Models

Javad Rajabi, Soroush Mehraban, Seyedmorteza Sadat, Babak Taati
NeurIPS 2025poster

Classifier-free guidance (CFG) has become an essential component of modern diffusion models to enhance both generation quality and alignment with input conditions. However, CFG requires specific training procedures and is limited to conditional generation. To address these limitations, we propose To…

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2023

Large Language Models are Fixated by Red Herrings: Exploring Creative Problem Solving and Einstellung Effect using the Only Connect Wall Dataset

Saeid Alavi Naeini, Raeid Saqur, mozhgan saeidi, John Michael Giorgi, Babak Taati
NeurIPS 2023poster

The quest for human imitative AI has been an enduring topic in AI research since inception. The technical evolution and emerging capabilities of the latest cohort of large language models (LLMs) have reinvigorated the subject beyond academia to cultural zeitgeist. While recent NLP evaluation benchm…

Cited by 14Source ↗PDF ↗code ↗Scholar ↗