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Found 10 papers, 3 papers with code
Motion-X++: A Large-Scale Multimodal 3D Whole-body Human Motion Dataset
To address this issue, we develop a scalable annotation pipeline that can automatically capture 3D whole-body human motion and comprehensive textural labels from RGB videos and build the Motion-X dataset comprising 81. 1K text-motion pairs.
ScaMo: Exploring the Scaling Law in Autoregressive Motion Generation Model
In this paper, we introduce a scalable motion generation framework that includes the motion tokenizer Motion FSQ-VAE and a text-prefix autoregressive transformer.
MotionCLR: Motion Generation and Training-free Editing via Understanding Attention Mechanisms
Previous motion diffusion models lack explicit modeling of the word-level text-motion correspondence and good explainability, hence restricting their fine-grained editing ability.
Story3D-Agent: Exploring 3D Storytelling Visualization with Large Language Models
Traditional visual storytelling is complex, requiring specialized knowledge and substantial resources, yet often constrained by human creativity and creation precision.
MotionLLM: Understanding Human Behaviors from Human Motions and Videos
This study delves into the realm of multi-modality (i. e., video and motion modalities) human behavior understanding by leveraging the powerful capabilities of Large Language Models (LLMs).
HumanTOMATO: Text-aligned Whole-body Motion Generation
This work targets a novel text-driven whole-body motion generation task, which takes a given textual description as input and aims at generating high-quality, diverse, and coherent facial expressions, hand gestures, and body motions simultaneously.
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Motion Synthesis
on Motion-X
Motion-X: A Large-scale 3D Expressive Whole-body Human Motion Dataset
In this paper, we present Motion-X, a large-scale 3D expressive whole-body motion dataset.
Learning to Linearize Deep Neural Networks for Secure and Efficient Private Inference
For a similar ReLU budget SENet can yield models with ~2. 32% improved classification accuracy, evaluated on CIFAR-100.
Sparse Mixture Once-for-all Adversarial Training for Efficient In-Situ Trade-Off Between Accuracy and Robustness of DNNs
Existing deep neural networks (DNNs) that achieve state-of-the-art (SOTA) performance on both clean and adversarially-perturbed images rely on either activation or weight conditioned convolution operations.
P2M-DeTrack: Processing-in-Pixel-in-Memory for Energy-efficient and Real-Time Multi-Object Detection and Tracking
The designs also reduce the sensor and total energy (obtained from in-house circuit simulations at Globalfoundries 22nm technology node) per frame by 5. 7x and 1. 14x, respectively.
