Motion Generation Benchmark

Motion generation benchmarks are datasets and evaluation frameworks designed to assess the performance of algorithms that create realistic human motion from various inputs like text, audio, or other motion data. Current research focuses on developing large-scale datasets, often incorporating multimodal data (e.g., text and audio), and employing advanced model architectures such as diffusion transformers and transformer decoders to generate whole-body motion with fine-grained control. These benchmarks are crucial for advancing the field of human motion synthesis, with applications ranging from animation and virtual reality to educational tools and assistive technologies. The development of more robust evaluation metrics is also a key area of ongoing work.