![]() This optimization is expressed in the SDF latent embedding, and hence can also be performed efficiently. On the decoder side, to avoid the typical artifacts of block-based coding, we also propose a variational optimization that compensates for quantization residuals in order to penalize unsightly discontinuities in the decompressed signal. This results in a collection of low entropy tuples that can be effectively quantized and symbolically encoded. a TSDF), such as those found in most rigid/non-rigid reconstruction pipelines, and efficiently projects each TSDF block onto the SDF latent space. We then propose an optimization that takes a Truncated SDF (i.e. a block), admit a low-dimensional embedding due to the innate geometric redundancies in their representation. ![]() ![]() We demonstrate how SDFs, when defined over a small local region (i.e. ![]() In contrast, we propose an encoder that leverages an implicit representation (namely a Signed Distance Function) to represent the observed geometry, as well as its changes through time. We note how much of the algorithmic complexity in traditional 4D compression arises from the necessity to encode geometry using an explicit model (i.e. We introduce a realtime compression architecture for 4D performance capture that is two orders of magnitude faster than current state-of-the-art techniques, yet achieves comparable visual quality and bitrate. ![]()
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