Narek Tumanyan

I am a PhD student in computer vision at the Weizmann Institute of Science, advised by Prof. Tali Dekel.
Previously, I was a Research Scientist Intern at Meta Reality Labs, advised by Jonathon Luiten.
My research interests are in image and video generation, self-supervised learning, and interpretability of vision foundation models for unveiling novel applications.

I completed my Master's at the Weizmann Institute in Tali Dekel's lab, and received my Bachelor's degree in Computer Science at the American University of Armenia.

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Research

We present DRoPS, a method for dynamic scene reconstruction from monocular videos that leverages a static pre-scan of the dynamic object as an explicit geometric and appearance prior. By organizing Gaussian primitives into surface-aligned pixel grids and modeling motion with a CNN-based Deep Motion Prior, DRoPS achieves SOTA in novel-view synthesis and long-range 3D tracking accuracy.

We present a novel self-supervised method for long-range dense tracking in video, which harnesses the powerful visual prior of DINO. By combining test-time training on a single input video with the semantic representations of DINO, DINO-Tracker reaches SOTA in tracking across long occlusions.

We present a new framework for text-driven image-to-image translation that harnesses the power of a pre-trained text-to-image diffusion model.

We observe and empirically demonstrate that fine-grained control over the generated structure can be achieved by manipulating spatial features and their self-attention inside the model.

We design a feed-forward model for real-time semantic appearance transfer that is directly conditioned on ViT features, allowing the model to utilize the powerful information they encode.

We present a method for semantically transferring the appearance of one natural image to another. We train a generator given only a single structure/appearance image pair as input. Our key idea is leveraging a pre-trained Vision Transformer as a semantic prior by deriving novel representations of structure and appearance from its feature space.

Talks

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