Synthesizing high-fidelity head avatars is a central problem for computer vision and graphics. While head avatar synthesis algorithms have advanced rapidly, the best ones still face great obstacles in real-world scenarios.

Inthiswork,wepropose sampling-argmax, adifferentiable training method that imposes implicit constraints tothe shape of the probability map by minimizing the expectation of the localization error.

Duetothe long-horizon nature ofthe task, we often heavily rely onvisual manuals that provide step-by-step guidance during the assembly process.

In this supplementary material, we first present the details of the conditional score network in Sec. 2. Sec. 4. Finally, we conduct experiments on Chinese MER dataset CH-SIMS [ I) which is subsequently fixed for the model (i.e., not learnable). Table 1: Hyperparameter settings in IMDer.Hyperparameter CMU-MOSI CMU-MOSEI Optimizer Adam Adam Batch size 32 128 Learning rate 0.001 0.002 σ used in our stochastic differential equation 25 25 Number of iterations for Euler-Maruyama solver 500 500 Shallow Feature Extractor Kernel size for E CH-SIMS contains 2281 refined video segments with fine-grained annotations of modalities. For vision modality, we use MultiComp OpenFace2.0 The experimental results are listed in the Tab. 3. Obviously, our proposed IMDer consistently achieves better results than MMIN or GCNet under random missing protocol.

We demonstrate qualitatively and quantitatively that our proposed approach is able tomodel the appearance ofindividual strokes,aswell asthe compositional structure oflargerdiagram drawings.