There is a growing interest in adopting Deep Learning (DL) given its superior performance in many domains. However, modern DL frameworks such as TensorFlow often come with a steep learning curve. In this work, we propose INTENT, an interactive system that infers user intent and generates corresponding TensorFlow code on behalf of users. INTENT helps users understand and validate the semantics of generated code by rendering individual tensor transformation steps with intermediate results and element-wise data provenance. Users can further guide INTENT by marking certain TensorFlow operators as desired or undesired, or directly manipulating the generated code. A within-subjects user study with 18 participants shows that users can finish programming tasks in TensorFlow more successfully with only half the time, compared with a variant of INTENT that has no interaction or visualization support.

VTubers are live streamers who embody computer animation virtual avatars. VTubing is a rapidly rising form of online entertainment in East Asia, most notably in Japan and China, and it has been more recently introduced in the West. However, animating an expressive VTuber avatar remains a challenge due to budget and usability limitations of current solutions, i.e., high-fidelity motion capture is expensive, while keyboard-based VTubing interfaces impose a cognitive burden on the streamer. This paper proposes a novel approach for VTubing animation based on the key principle of loosening the coupling between the VTuber and their avatar, and it describes a first implementation of the approach in the AlterEcho VTubing animation system. AlterEcho generates expressive VTuber avatar animation automatically, without the streamer’s explicit intervention; it breaks the strict tethering of the avatar from the streamer, allowing the avatar’s nonverbal behavior to deviate from that of the streamer. Without the complete independence of a true alter ego, but also without the constraint of mirroring the streamer with the fidelity of an echo, AlterEcho produces avatar animations that have been rated significantly higher by VTubers and viewers (N = 315) compared to animations created using simple motion capture, or using VMagicMirror, a state-of-the-art keyboard-based VTubing system. Our work also opens the door to personalizing the avatar persona for individual viewers.