On the other hand, rhythmic dynamics are synced with the speech prosody. On one hand, diverse pose modes are generated by conditional sampling in a latent space, guided by speech semantics. Accordingly, we introduce a novel freeform motion generation model (FreeMo) by equipping a two-stream architecture, i.e., a pose mode branch for primary posture generation, and a rhythmic motion branch for rhythmic dynamics synthesis. Motivated by studies in linguistics, we decompose the co-speech motion into two complementary parts: pose modes and rhythmic dynamics. Most existing works map speech to motion in a deterministic way by conditioning on certain styles, leading to sub-optimal results. Body motion generation from speech is inherently difficult due to the non-deterministic mapping from speech to body motions. People naturally conduct spontaneous body motions to enhance their speeches while giving talks. Extensive experiments and human evaluation demonstrate that the proposed method renders realistic co-speech gestures and outperforms previous methods in a clear margin. To enhance the quality of synthesized gestures, we develop a contrastive learning strategy based on audio-text alignment for better audio representations. A Hierarchical Pose Inferer subsequently renders the entire human pose gradually in a hierarchical manner. In HA2G, a Hierarchical Audio Learner extracts audio representations across semantic granularities. To fully utilize the rich connections between speech audio and human gestures, we propose a novel framework named Hierarchical Audio-to-Gesture (HA2G) for co-speech gesture generation. One observation is that the hierarchical semantics in speech and the hierarchical structures of human gestures can be naturally described into multiple granularities and associated together. Such a straightforward pipeline fails to generate fine-grained co-speech gestures. Previous studies often synthesize pose movement in a holistic manner, where poses of all joints are generated simultaneously. Preview Video, Images, Footages and Music are not part of the product.Generating speech-consistent body and gesture movements is a long-standing problem in virtual avatar creation. This Videohive explainer video toolkit has pre-animated backgrounds and pre-animated stories for quick understanding.Įxplainer video toolkit template includes more than 350 animated elements including 50+ characters, 50 elements and much more.Įasy to use drag and drop feature makes Toon City a great explainer video toolkit for professional explainer videos.įree download here: Images 1, Images 2, Images 3Ĭompletely Scalable – Resize Without Losing QualityĪfter Effects CS5.5 CS6, CC, CC2014, CC2015, cc2017 Our premium after effects explainer video kit includes 80+ icons and elements which are fully customizable.Įxplainer video after effects of Toon City includes expressive characters with automatic lip syncing Toon city is a smart explainer video toolkit template that comes with stylish modern hand gesture for 12+ mobile tablets. Overview and features of explainer video script template: In short, something phenomenal for graphical presentations, animation storytelling, and visual effects. Toon city is fully customizable explainer video toolkit template specially designed for detailed explanation with smooth animation. Our ultimate explainer video toolkit comes with 350+ animated items and 1920×1080 resolution(characters are in 2000×2000 pixel). Toon City – the explainer video toolkit from Iron Network – helps businesses and creative professionals tell their stories through different animation characters and titles.
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