patch-gg/crowdbricks

选用指南

AI 根据 README 整理 · 2026/10/28 16:47:53

使用方式

通过 Python API 使用，可运行示例脚本如 examples/futures.py。

查看 README 依据

../motionbricks/.venv/bin/python examples/futures.py --record futures.take ../motionbricks/.venv/bin/python examples/horde.py --record horde.take ``` The first run of each batch size and precision builds a TensorRT plan into `trt-cache/`, which takes a few minutes. To watch a take, export the character mesh from your checkout once, then serve the folder: ```sh ../motionbricks/.venv/bin/python viewer/export_character.py python3 -m http.server 8000 ``` and open `http://localhost:8000/viewer/?take=../futures.take`, or drop a take on the page. ## Use it ```python import warp as wp

from app.runtime.common.rig import load_rig_assets from motionbricks.model.contract import ModelRig from crowdbricks import Approach, BatchGenerator, Crowd, GaitLibrary, Obstacle, Stand, Walk, obstacle_pool wp.init() assets = load_rig_assets(ModelRig.SOMA) generator = BatchGenerator(assets, batch=16, capacity=64, cache_dir="trt-cache") gaits = GaitLibrary(assets, generator.stream) # idle, walk, jog, run and upstream's six style gaits crowd = Crowd(generator, gaits) # A wall to vault, as upstream's jump_0_75m Smart Primitive, facing an approach along +x.

部署要求

需要 NVIDIA GPU、Git LFS 和 uv，并基于 MotionBricks 的 checkout 运行。

查看 README 依据

crowdbricks runs on top of a MotionBricks checkout. Set that up first, as its README says (an NVIDIA GPU, [Git LFS](https://git-lfs.com/) and [uv](https://docs.astral.sh/uv/)): ```sh git clone https://github.com/NVlabs/motionbricks cd motionbricks git lfs pull uv sync cd .. ``` Then run crowdbricks with MotionBricks' Python and its checkout on `PYTHONPATH` (crowdbricks uses its `app` package, which it does not install): ```sh git clone https://github.com/patch-gg/crowdbricks cd crowdbricks export PYTHONPATH=$PWD/../motionbricks

已知限制

不支持 IK 钉住手脚、不播放空闲和坐姿剪辑，FP16 下可能与上游存在细微差异。

查看 README 依据

- **What it leaves out.** Upstream's character runtime also pins hands and feet to targets with IK (vault hands may land a little off the wall here), plays idle and sitting clips (a crowd character generates toward the idle target instead), and smooths input with springs. crowdbricks keeps a character to a few numbers and a driver so a whole crowd steps in about the time upstream takes for one. ## Matches upstream

FP16 is looser, for upstream as much as for crowdbricks. The pose module picks discrete codebook tokens, and on some requests FP16 rounding flips a pick: moving upstream's own context by 10 micrometers turns a jog's joints by 6 degrees, and turning a run's target by a thousandth of a degree moves a joint by 15 (both ends stay pinned by the endpoint correction). Each batch size is also its own plan with its own kernels. The FP16 test therefore asks bigger batches for the same length of motion and close agreement on average. ```sh ../motionbricks/.venv/bin/python -m pytest tests/ -s ```