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Alexander Sax

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4 papers

2 author rows

ICML Conference 2025 Conference Paper

From Thousands to Billions: 3D Visual Language Grounding via Render-Supervised Distillation from 2D VLMs

Ang Cao
Sergio Arnaud
Oleksandr Maksymets
Jianing Yang
Ayush Jain
Ada Martin
Vincent-Pierre Berges
Paul McVay

3D vision-language grounding faces a fundamental data bottleneck: while 2D models train on billions of images, 3D models have access to only thousands of labeled scenes–a six-order-of-magnitude gap that severely limits performance. We introduce LIFT-GS, a practical distillation technique that overcomes this limitation by using differentiable rendering to bridge 3D and 2D supervision. LIFT-GS predicts 3D Gaussian representations from point clouds and uses them to render predicted language-conditioned 3D masks into 2D views, enabling supervision from 2D foundation models (SAM, CLIP, LLaMA) without requiring any 3D annotations. This render-supervised formulation enables end-to-end training of complete encoder-decoder architectures and is inherently model-agnostic. LIFT-GS achieves state-of-the-art results with 25. 7% mAP on open-vocabulary instance segmentation (vs. 20. 2% prior SOTA) and consistent 10-30% improvements on referential grounding tasks. Remarkably, pretraining effectively multiplies fine-tuning datasets by 2$\times$, demonstrating strong scaling properties that suggest 3D VLG currently operates in a severely data-scarce regime. Project page: https: //liftgs. github. io.

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ICML Conference 2025 Conference Paper

LOCATE 3D: Real-World Object Localization via Self-Supervised Learning in 3D

Paul McVay
Sergio Arnaud
Ada Martin
Arjun Majumdar
Krishna Murthy Jatavallabhula
Phillip Thomas
Ruslan Partsey
Daniel Dugas

We present LOCATE 3D, a model for localizing objects in 3D scenes from referring expressions like "the small coffee table between the sofa and the lamp. " LOCATE 3D sets a new state-of-the-art on standard referential grounding benchmarks and showcases robust generalization capabilities. Notably, LOCATE 3D operates directly on sensor observation streams (posed RGB-D frames), enabling real-world deployment on robots and AR devices. Key to our approach is 3D-JEPA, a novel self-supervised learning (SSL) algorithm applicable to sensor point clouds. It takes as input a 3D pointcloud featurized using 2D foundation models (CLIP, DINO). Subsequently, masked prediction in latent space is employed as a pretext task to aid the self-supervised learning of contextualized pointcloud features. Once trained, the 3D-JEPA encoder is finetuned alongside a language-conditioned decoder to jointly predict 3D masks and bounding boxes. Additionally, we introduce LOCATE 3D DATASET, a new dataset for 3D referential grounding, spanning multiple capture setups with over 130K annotations. This enables a systematic study of generalization capabilities as well as a stronger model. Code, models and dataset can be found at the project website: locate3d. atmeta. com

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ICML Conference 2025 Conference Paper

Unifying 2D and 3D Vision-Language Understanding

Ayush Jain
Alexander Swerdlow
Yuzhou Wang
Sergio Arnaud
Ada Martin
Alexander Sax
Franziska Meier
Katerina Fragkiadaki

Progress in 3D vision-language learning has been hindered by the scarcity of large-scale 3D datasets. We introduce UniVLG, a unified architecture for 2D and 3D vision-language understanding that bridges the gap between existing 2D-centric models and the rich 3D sensory data available in embodied systems. Our approach initializes most model weights from pre-trained 2D models and trains on both 2D and 3D vision-language data. We propose a novel language-conditioned mask decoder shared across 2D and 3D modalities to ground objects effectively in both RGB and RGB-D images, outperforming box-based approaches. To further reduce the domain gap between 2D and 3D, we incorporate 2D-to-3D lifting strategies, enabling UniVLG to utilize 2D data to enhance 3D performance. With these innovations, our model achieves state-of-the-art performance across multiple 3D vision-language grounding tasks, demonstrating the potential of transferring advances from 2D vision-language learning to the data-constrained 3D domain. Furthermore, co-training on both 2D and 3D data enhances performance across modalities without sacrificing 2D capabilities. By removing the reliance on 3D mesh reconstruction and ground-truth object proposals, UniVLG sets a new standard for realistic, embodied-aligned evaluation. Code and additional visualizations are available at https: //univlg. github. io.

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IJCAI Conference 2019 Conference Paper

Taskonomy: Disentangling Task Transfer Learning

Amir Zamir
Alexander Sax
William Shen
Leonidas Guibas
Jitendra Malik
Silvio Savarese

Do visual tasks have relationships, or are they unrelated? For instance, could having surface normals simplify estimating the depth of an image? Intuition answers these questions positively, implying existence of a certain structure among visual tasks. Knowing this structure has notable values; it provides a principled way for identifying relationships across tasks, for instance, in order to reuse supervision among tasks with redundancies or solve many tasks in one system without piling up the complexity. We propose a fully computational approach for modeling the transfer learning structure of the space of visual tasks. This is done via finding transfer learning dependencies across tasks in a dictionary of twenty-six 2D, 2. 5D, 3D, and semantic tasks. The product is a computational taxonomic map among tasks for transfer learning, and we exploit it to reduce the demand for labeled data. For example, we show that the total number of labeled datapoints needed for solving a set of 10 tasks can be reduced by roughly 2/3 (compared to training independently) while keeping the performance nearly the same. We provide a set of tools for computing and visualizing this taxonomical structure at http: //taskonomy. vision.

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