Arkaprava Sinha

Weston Bondurant, Arkaprava Sinha, Hieu Le et al.

Diffusion-based approaches have recently achieved strong results in face swapping, offering improved visual quality over traditional GAN-based methods. However, even state-of-the-art models often suffer from fine-grained artifacts and poor identity preservation, particularly under challenging poses and expressions. A key limitation of existing approaches is their failure to meaningfully leverage 3D facial structure, which is crucial for disentangling identity from pose and expression. In this work, we propose DiffSwap++, a novel diffusion-based face-swapping pipeline that incorporates 3D facial latent features during training. By guiding the generation process with 3D-aware representations, our method enhances geometric consistency and improves the disentanglement of facial identity from appearance attributes. We further design a diffusion architecture that conditions the denoising process on both identity embeddings and facial landmarks, enabling high-fidelity and identity-preserving face swaps. Extensive experiments on CelebA, FFHQ, and CelebV-Text demonstrate that DiffSwap++ outperforms prior methods in preserving source identity while maintaining target pose and expression. Additionally, we introduce a biometric-style evaluation and conduct a user study to further validate the realism and effectiveness of our approach. Code will be made publicly available at https://github.com/WestonBond/DiffSwapPP

Arkaprava Sinha, Monish Soundar Raj, Pu Wang et al.

Temporal Action Detection (TAD) in untrimmed videos requires models that can efficiently (1) process long-duration videos, (2) capture temporal variations within action classes, and (3) handle dense, overlapping actions, all while remaining suitable for resource-constrained edge deployment. While Transformer-based methods achieve high accuracy, their quadratic complexity hinders deployment in such scenarios. Given the recent popularity of linear complexity Mamba-based models, leveraging them for TAD is a natural choice. However, naively adapting Mamba from language or vision tasks fails to provide an optimal solution and does not address the challenges of long, untrimmed videos. Therefore, we propose Multi-Scale Temporal Mamba (MS-Temba), the first Mamba-based architecture specifically designed for densely labeled TAD tasks. MS-Temba features Temporal Mamba Blocks (Temba Blocks), consisting of Temporal Convolutional Module (TCM) and Dilated SSM (D-SSM). TCM captures short-term dependencies using dilated convolutions, while D-SSM introduces a novel dilated state-space mechanism to model long-range temporal relationships effectively at each temporal scale. These multi-scale representations are aggregated by Scale-Aware State Fuser, which learns a unified representation for detecting densely overlapping actions. Experiments show that MS-Temba achieves state-of-the-art performance on long-duration videos, remains competitive on shorter segments, and reduces model complexity by 88%. Its efficiency and effectiveness make MS-Temba well-suited for real-world edge deployment.

Arkaprava Sinha, Dominick Reilly, Francois Bremond et al.

The introduction of vision-language models like CLIP has enabled the development of foundational video models capable of generalizing to unseen videos and human actions. However, these models are typically trained on web videos, which often fail to capture the challenges present in Activities of Daily Living (ADL) videos. Existing works address ADL-specific challenges, such as similar appearances, subtle motion patterns, and multiple viewpoints, by combining 3D skeletons and RGB videos. However, these approaches are not integrated with language, limiting their ability to generalize to unseen action classes. In this paper, we introduce SKI models, which integrate 3D skeletons into the vision-language embedding space. SKI models leverage a skeleton-language model, SkeletonCLIP, to infuse skeleton information into Vision Language Models (VLMs) and Large Vision Language Models (LVLMs) through collaborative training. Notably, SKI models do not require skeleton data during inference, enhancing their robustness for real-world applications. The effectiveness of SKI models is validated on three popular ADL datasets for zero-shot action recognition and video caption generation tasks.

Dominick Reilly, Rajatsubhra Chakraborty, Arkaprava Sinha et al.

Current Large Language Vision Models (LLVMs) trained on web videos perform well in general video understanding but struggle with fine-grained details, complex human-object interactions (HOI), and view-invariant representation learning essential for Activities of Daily Living (ADL). This limitation stems from a lack of specialized ADL video instruction-tuning datasets and insufficient modality integration to capture discriminative action representations. To address this, we propose a semi-automated framework for curating ADL datasets, creating ADL-X, a multiview, multimodal RGBS instruction-tuning dataset. Additionally, we introduce LLAVIDAL, an LLVM integrating videos, 3D skeletons, and HOIs to model ADL's complex spatiotemporal relationships. For training LLAVIDAL a simple joint alignment of all modalities yields suboptimal results; thus, we propose a Multimodal Progressive (MMPro) training strategy, incorporating modalities in stages following a curriculum. We also establish ADL MCQ and video description benchmarks to assess LLVM performance in ADL tasks. Trained on ADL-X, LLAVIDAL achieves state-of-the-art performance across ADL benchmarks. Code and data will be made publicly available at: https://adl-x.github.io/.