Portrait de Amirhossein Zamani

Amirhossein Zamani

Doctorat - Concordia
Superviseur⋅e principal⋅e
Eugene Belilovsky
Co-supervisor
Blake Richards
Sujets de recherche
Apprentissage par renforcement
Apprentissage profond
Infographie 3D
Interaction humain-IA
Modèles génératifs
Robotique
Vision par ordinateur
Site web
Google Scholar
GitHub

Publications

Path-independent Flow Matching for Multi-parameter Generative Dynamics
Francisco Tellez
Amirhossein Zamani
Philippe Martin
Shuang Ni
Guy Wolf
Eugene Belilovsky
Sina Sanjari
Yanlei Zhang
Flow Matching is a powerful framework for learning transport maps between probability distributions. Yet its standard single-parameter formu… (voir plus)lation is not designed to capture multi-parameter variations where the resulting transport should be path-independent. Path independence is crucial because it ensures that transformations depend only on the initial and target distributions, not on the specific path. In this work, we introduce Path-independent Flow Matching (PiFM), a method for learning vector fields whose induced flows yield path-independent transport between distributions. We show that PiFM generalizes Flow Matching to higher-dimensional parameter domains while enforcing structural conditions that ensure consistency of composed transformations. In addition, we show that, under suitable assumptions, PiFM approximates the Wasserstein barycenter, linking the framework to a notion of distributional interpolation. To enable practical training, we propose a tractable, simulation-free objective that regresses onto multi-parameter conditional probability paths. We showcase empirically that PiFM outperforms other approaches on both synthetic and real world data in interpolating path-independent trajectories and generating desired out of distribution samples.
2026-05-12
arXiv (prépublication)
doi.org
openreview.net
End-to-End Fine-Tuning of 3D Texture Generation using Differentiable Rewards
Amirhossein Zamani
Tianhao Xie
Amir G. Aghdam
Tiberiu Popa
Eugene Belilovsky
While recent 3D generative models can produce high-quality texture images, they often fail to capture human preferences or meet task-specifi… (voir plus)c requirements. Moreover, a core challenge in the 3D texture generation domain is that most existing approaches rely on repeated calls to 2D text-to-image generative models, which lack an inherent understanding of the 3D structure of the input 3D mesh object. To alleviate these issues, we propose an end-to-end differentiable, reinforcement-learning-free framework that embeds human feedback, expressed as differentiable reward functions, directly into the 3D texture synthesis pipeline. By back-propagating preference signals through both geometric and appearance modules of the proposed framework, our method generates textures that respect the 3D geometry structure and align with desired criteria. To demonstrate its versatility, we introduce three novel geometry-aware reward functions, which offer a more controllable and interpretable pathway for creating high-quality 3D content from natural language. By conducting qualitative, quantitative, and user-preference evaluations against state-of-the-art methods, we demonstrate that our proposed strategy consistently outperforms existing approaches. Our implementation code is publicly available at: https://github.com/AHHHZ975/Differentiable-Texture-Learning
2026-03-05
IEEE/CVF Winter Conference on Applications of Computer Vision (publié)
doi.org
arxiv.org
Geometry-Aware Preference Learning for 3D Texture Generation
Amirhossein Zamani
Tianhao Xie
Amir Aghdam
Tiberiu Popa
Eugene Belilovsky
Recent advances in 3D generative models have achieved impressive results but 3D contents generated by these models may not align with subjec… (voir plus)tive human preferences or task-specific criteria. Moreover, a core challenge in the 3D texture generation domain remains: most existing approaches rely on repeated calls to 2D text-to-image generative models, which lack an inherent understanding of the 3D structure of the input 3D mesh object. To address this, we propose an end-to-end differentiable preference learning framework that back-propagates human preferences, represented by differentiable reward functions, through the entire 3D generative pipeline, making the process inherently geometry-aware. We demonstrate the effectiveness of our framework using four proposed novel geometry-aware reward functions, offering a more controllable and interpretable pathway for high-quality 3D content creation from natural language.
2025-06-09
ICML.cc/2025/Workshop/MoFA (poster)
openreview.net
openreview.net