56 citations · 121 across the 49 of their papers we have counts for
31 papers · 1 filter
From Tokens to Trajectories: Parameter-Efficient Continuous Adaptation of Unified Multimodal Models
Gayatri Deshmukh, Onkar Susladkar, Adheesh Juvekar +2
While discrete tokenization in Unified Multimodal Models (UMMs) establishes strong semantic reasoning, it introduces a strict quantization bottleneck that degrades fine-grained vis…
SILSA: Sliding-Window Slice Latents for Topology-Preserving High-Resolution 3D Generation
Tianjiao Yu, Xinzhuo Li, Yifan Shen +4
High-resolution 3D generation increasingly relies on voxel latents and multi-stage pipelines that first predict active structure and then synthesize local geometry. While effective…
One Editor, Many Edits: A Unified Training-Free Framework for Diverse Video Editing
Adheesh Sunil Juvekar, Onkar Kishor Susladkar, Kiet A. Nguyen +6
Video editing spans diverse editing paradigms, yet achieving high-quality instruction-guided and subject-guided editing within a single unified framework remains challenging. We in…
GraphVid: Interactive Graph-Controllable Video Generation
Vedant Shah, Onkar Susladkar, Tushar Prakash +5
Controllable video generation remains challenging due to the difficulty of specifying precise multi-object interactions using text prompts or motion-control inputs that primarily c…
Decoding Children's Gait Behavior
Yifan Shen, Boyi Li, Meihuan Huang +12
We introduce a new problem domain for human action recognition: the fine-grained analysis of children's gait behaviors from standard RGB video. We specifically target the ambulator…
ELSA3D: Elastic Semantic Anchoring for Unified 3D Understanding and Generation
Tianjiao Yu, Xinzhuo Li, Yifan Shen +4
Unified 3D foundation models aspire to generate 3D assets and reason about them in language within a single backbone, but their text-3D interaction remains largely implicit. Existi…
CogniRoute: Learning to Route Social Evidence in Omni-Modal Models
Yifan Shen, Pei Tian, Xinzhuo Li +8
Omni-modal models can ingest video, audio, and text, but unified access to multiple modalities does not guarantee that a model uses the right evidence. This gap is especially prono…
Phantom: Physics-Infused Video Generation via Joint Modeling of Visual and Latent Physical Dynamics
Ying Shen, Jerry Xiong, Tianjiao Yu +1
Recent advances in generative video modeling, driven by large-scale datasets and powerful architectures, have yielded remarkable visual realism. However, emerging evidence suggests…
3D-VCD: Hallucination Mitigation in 3D-LLM Embodied Agents through Visual Contrastive Decoding
Makanjuola Ogunleye, Eman Abdelrahman, Ismini Lourentzou
Large multimodal models are increasingly used as the reasoning core of embodied agents operating in 3D environments, yet they remain prone to hallucinations that can produce unsafe…
RewardFlow: Generate Images by Optimizing What You Reward
Onkar Susladkar, Dong-Hwan Jang, Tushar Prakash +7
We introduce RewardFlow, an inversion-free framework that steers pretrained diffusion and flow-matching models at inference time through multi-reward Langevin dynamics. RewardFlow…
EgoForge: Goal-Directed Egocentric World Simulator
Yifan Shen, Jiateng Liu, Xinzhuo Li +9
Generative world models have shown promise for simulating dynamic environments, yet egocentric video remains challenging due to rapid viewpoint changes, frequent hand-object intera…
DreamPartGen: Semantically Grounded Part-Level 3D Generation via Collaborative Latent Denoising
Tianjiao Yu, Xinzhuo Li, Muntasir Wahed +4
Understanding and generating 3D objects as compositions of meaningful parts is fundamental to human perception and reasoning. However, most text-to-3D methods overlook the semantic…
PyraTok: Language-Aligned Pyramidal Tokenizer for Video Understanding and Generation
Onkar Susladkar, Tushar Prakash, Adheesh Juvekar +4
Discrete video VAEs underpin modern text-to-video generation and video understanding systems, yet existing tokenizers typically learn visual codebooks at a single scale with limite…
Toward Cognitive Supersensing in Multimodal Large Language Model
Boyi Li, Yifan Shen, Yuanzhe Liu +12
Multimodal Large Language Models (MLLMs) have achieved remarkable success in open-vocabulary perceptual tasks, yet their ability to solve complex cognitive problems remains limited…
Best of Both Worlds: Multimodal Reasoning and Generation via Unified Discrete Flow Matching
Onkar Susladkar, Tushar Prakash, Gayatri Deshmukh +8
We propose UniDFlow, a unified discrete flow-matching framework for multimodal understanding, generation, and editing. It decouples understanding and generation via task-specific l…
CoRe3D: Collaborative Reasoning as a Foundation for 3D Intelligence
Tianjiao Yu, Xinzhuo Li, Yifan Shen +2
Recent advances in large multimodal models suggest that explicit reasoning mechanisms play a critical role in improving model reliability, interpretability, and cross-modal alignme…
Open World Scene Graph Generation using Vision Language Models
Amartya Dutta, Kazi Sajeed Mehrab, Medha Sawhney +8
Scene-Graph Generation (SGG) seeks to recognize objects in an image and distill their salient pairwise relationships. Most methods depend on dataset-specific supervision to learn t…
Fine-Grained Preference Optimization Improves Spatial Reasoning in VLMs
Yifan Shen, Yuanzhe Liu, Jingyuan Zhu +6
Current Vision-Language Models (VLMs) struggle with fine-grained spatial reasoning, particularly when multi-step logic and precise spatial alignment are required. In this work, we…
Counterfactual Segmentation Reasoning: Diagnosing and Mitigating Pixel-Grounding Hallucination
Xinzhuo Li, Adheesh Juvekar, Jiaxun Zhang +6
Segmentation Vision-Language Models (VLMs) have significantly advanced grounded visual understanding, yet they remain prone to pixel-grounding hallucinations, producing masks for i…
PartGS: Part-aware Modeling of Articulated Objects using 3D Gaussian Splatting
Tianjiao Yu, Vedant Shah, Muntasir Wahed +3
Articulated objects are common in the real world, yet modeling their structure and motion remains a challenging task for 3D reconstruction methods. In this work, we introduce Part$…
LaTtE-Flow: Layerwise Timestep-Expert Flow-based Transformer
Ying Shen, Zhiyang Xu, Jiuhai Chen +6
Recent advances in multimodal foundation models unifying image understanding and generation have opened exciting avenues for tackling a wide range of vision-language tasks within a…
CALICO: Part-Focused Semantic Co-Segmentation with Large Vision-Language Models
Kiet A. Nguyen, Adheesh Juvekar, Tianjiao Yu +2
Recent advances in Large Vision-Language Models (LVLMs) have enabled general-purpose vision tasks through visual instruction tuning. While existing LVLMs can generate segmentation…
PRIMA: Multi-Image Vision-Language Models for Reasoning Segmentation
Muntasir Wahed, Kiet A. Nguyen, Adheesh Sunil Juvekar +6
Despite significant advancements in Large Vision-Language Models (LVLMs)' capabilities, existing pixel-grounding models operate in single-image settings, limiting their ability to…
RAVEL: Rare Concept Generation and Editing via Graph-driven Relational Guidance
Kavana Venkatesh, Yusuf Dalva, Ismini Lourentzou +1
Despite impressive visual fidelity, current text-to-image (T2I) diffusion models struggle to depict rare, complex, or culturally nuanced concepts due to training data limitations.…