Release unimate_uniml3d_f60_v2_full_cross_attn and add the MIT license; tighten the model card and shrink the teaser

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ng6SrZiu3mbKQYdpQiXALV

.gitattributes

@@ -225,3 +225,4 @@ unimate_uniml3d_f60_v2/samples/step_100000/mixamo-3_fk.mp4 filter=lfs diff=lfs m
 unimate_uniml3d_f60_v2/samples/step_100000/mixamo-3_ric.mp4 filter=lfs diff=lfs merge=lfs -text
 unimate_uniml3d_f60_v2/samples/step_100000/Rhino-0_fk.mp4 filter=lfs diff=lfs merge=lfs -text
 unimate_uniml3d_f60_v2/samples/step_100000/Rhino-0_ric.mp4 filter=lfs diff=lfs merge=lfs -text
+unimate_uniml3d_f60_v2_full_cross_attn/samples/**/*.mp4 filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -2,6 +2,7 @@
 */debug/
 */slurm_*.out
 */slurm_*.err
+*/*.sbatch
 # Training samples: only the videos are uploaded
 */samples/**/*.npy
 */samples/tpose/

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2026 Linzhan Mou
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,4 +1,7 @@
 ---
+license: mit
+language:
+- en
 library_name: pytorch
 datasets:
 - Linzhan/UniML3D
@@ -12,41 +15,45 @@ tags:
 - arxiv:2609.05415
 ---
 
-# UniMate Checkpoints
+# UniMate
 
-Pretrained checkpoints for **UniMate: One Unified Model to Animate Diverse Skeletons** (SIGGRAPH Asia 2026). UniMate is a single text-conditioned flow-matching model that generates motion for skeletons of any topology: animals, humanoids and rigged objects.
+**One Unified Model to Animate Diverse Skeletons** (SIGGRAPH Asia 2026)
 
 [Project Page](https://linzhanmou.com/unimate/) · [Paper](https://arxiv.org/abs/2609.05415) · [Video](https://youtu.be/xbndC-dEuVw) · [Code](https://github.com/Friedrich-M/UniMate) · [Dataset](https://huggingface.co/datasets/Linzhan/UniML3D) · [Interactive Demo](https://linzhanmou.com/unimate/interactive.html)
 
 ![UniMate teaser](teaser.png)
 
+Pretrained checkpoints for UniMate, a text-conditioned flow-matching model that generates motion for skeletons of arbitrary topology: animals, humanoids and rigged objects.
+
 ## Models
 
-| Model | Training data | Joints per skeleton | Steps | Params | Recommended checkpoint | Status |
+| Model | Architecture | Training data | Joints | Steps | Params¹ | Status |
 |---|---|---|---|---|---|---|
-| [`unimate_uniml3d_f60_v2`](unimate_uniml3d_f60_v2) | UniML3D, release of 2026-09-27 | 5 to 70 | 100k | 74.1M | `checkpoint_step_100000.pt` | **Recommended** |
-| [`unimate_uniml3d_f60_preview`](unimate_uniml3d_f60_preview) | UniML3D, earlier build | 5 to 60 | 120k | 74.1M | `checkpoint_step_120000.pt` | Superseded |
-
-Both models share one architecture: graph attention over joints with AdaLN text conditioning, 10 layers of width 512, and a `google/flan-t5-base` text encoder. They generate 60-frame clips (2 seconds at 30 fps) and are trained on all three sources of [UniML3D](https://huggingface.co/datasets/Linzhan/UniML3D): Truebones ZOO animals, Mixamo humanoids and rigged Objaverse-XL objects.
-
-### `unimate_uniml3d_f60_v2`
+| [`unimate_uniml3d_f60_v2`](unimate_uniml3d_f60_v2) | graph attention, AdaLN text | UniML3D [`faaa817`](https://huggingface.co/datasets/Linzhan/UniML3D/tree/faaa81773b315247b03f183548e3898dbec2ce80) (2026-09-27) | 5–70 | 100k | 74.1M | **Recommended** |
+| [`unimate_uniml3d_f60_v2_full_cross_attn`](unimate_uniml3d_f60_v2_full_cross_attn) | full attention, cross-attention text | UniML3D `faaa817` (2026-09-27) | 5–70 | 100k | 66.2M | Variant |
+| [`unimate_uniml3d_f60_preview`](unimate_uniml3d_f60_preview) | graph attention, AdaLN text | UniML3D, pre-release build | 5–60 | 120k | 74.1M | Superseded |
 
-The recommended model. It is trained with `configs/uniml3d_60frames_graph_adaln_v2.json` on the UniML3D release of 2026-09-27 (dataset revision [`faaa817`](https://huggingface.co/datasets/Linzhan/UniML3D/tree/faaa81773b315247b03f183548e3898dbec2ce80)). Compared with the preview model, it:
+¹ Denoiser only. The frozen `google/flan-t5-base` text encoder is downloaded from the Hub on first use.
 
-- **covers more skeletons.** The joint limit is raised from 60 to 70, so 98.1% of the training clips are kept instead of 86.9%. This includes 70 of the 74 Truebones species.
-- **samples the datasets more evenly.** Datasets are balanced before object types (`sampler_dataset_alpha = 0.25`), so Truebones, Mixamo and Objaverse-XL make up about 13%, 24% and 63% of the training samples. Before, the single Mixamo rig got under 1% of them.
+Use each model's final checkpoint, `checkpoints/checkpoint_step_<steps>.pt`.
 
-Training: 100k steps on 8 NVIDIA H100 GPUs (about 22 hours), batch size 16 per GPU.
+**v2 versus preview.** Raising the joint limit from 60 to 70 keeps 98.1% of the training clips instead of 86.9%, and 70 of the 74 Truebones species. Datasets are balanced before object types (`sampler_dataset_alpha = 0.25`), so Truebones, Mixamo and Objaverse-XL make up about 13%, 24% and 63% of the training samples; the single Mixamo rig previously got under 1%.
 
-### `unimate_uniml3d_f60_preview`
+## Model details
 
-The first public model. It is trained with `configs/uniml3d_60frames_graph_adaln.json` on an earlier build of UniML3D, made before the 2026-09-27 release, so its training data differ from the current dataset. Training: 120k steps on 6 NVIDIA H100 GPUs (about 23 hours), batch size 16 per GPU. It is kept for reference; use `unimate_uniml3d_f60_v2` for new work.
+| | |
+|---|---|
+| **Architecture** | Transformer denoiser, 10 layers, width 512, 8 heads; flow matching with a linear path and velocity prediction |
+| **Inputs** | English motion description; the target skeleton's T-pose, joint hierarchy and joint names |
+| **Output** | 60 frames at 30 fps, 12 features per joint: 3-D position, 6-D rotation relative to the T-pose, 3-D velocity. The root joint encodes height, facing and planar velocity instead |
+| **Text encoder** | `google/flan-t5-base`, frozen |
+| **Guidance** | Classifier-free guidance on the caption: 10% caption dropout in training, default scale 3.0 |
 
 ## Quick start
 
-**1. Install the code.** Clone the [code repository](https://github.com/Friedrich-M/UniMate) and set up its `unimate` environment as described in its README. Run every command below from the repository root.
+Run every command from the root of the [code repository](https://github.com/Friedrich-M/UniMate), with its `unimate` environment installed.
 
-**2. Prepare the dataset features.** At sampling time, the model reads each target skeleton (its T-pose, topology and joint names) from `dataset/features/<dataset>/`. The Hub dataset ships the stage 1-3 annotations only, so build the features locally with stage 4 of the [data pipeline](https://github.com/Friedrich-M/UniMate/tree/main/data_process). Use the dataset revision the model was trained on:
+**1. Build the dataset features.** The sampler reads each target skeleton from `dataset/features/<dataset>/`. The Hub dataset ships the stage 1-3 export but not these stage-4 features, so build them from the revision the model was trained on:
 
 ```bash
 hf download Linzhan/UniML3D --repo-type dataset \
@@ -54,9 +61,9 @@ hf download Linzhan/UniML3D --repo-type dataset \
 bash data_process/scripts/run_extract_features.sh truebones   # likewise mixamo, objaverse
 ```
 
-The Truebones motion files come from a commercial pack and are not redistributed; see the dataset card for how to rebuild them.
+The Truebones motions come from a commercial pack and are not on the Hub; the dataset card explains how to rebuild them.
 
-**3. Download a model.** This downloads the recommended checkpoint only:
+**2. Download a model.** This fetches the configuration, normalization statistics and final checkpoint of the recommended model:
 
 ```bash
 hf download Linzhan/UniMate \
@@ -65,17 +72,16 @@ hf download Linzhan/UniMate \
     --local-dir outputs
 ```
 
-**4. Generate motion from text.** Write the prompts to a JSON file whose keys are `<object_type>-<case_id>`:
+**3. Generate motion from text.** Write the prompts to `test_cases.json` with keys `<object_type>-<case_id>`. `object_type` is a skeleton in `dataset/features/`: a Truebones species, `mixamo`, or an Objaverse-XL object ID. `case_id` is a free-form tag that names the output files.
 
 ```json
 {
-  "Horse-0": "A horse gallops forward.",
-  "mixamo-0": "A person jumps and waves both arms."
+  "Horse-0": "An object rears up on its hind legs.",
+  "mixamo-0": "An object jumps in place with both arms raised.",
+  "144367de23534c28ad2e83fc8abbd9ea-0": "An object flaps its wings."
 }
 ```
 
-Then run:
-
 ```bash
 python -m unimate.inference.sample \
     --exp_dir outputs/unimate_uniml3d_f60_v2 \
@@ -83,20 +89,33 @@ python -m unimate.inference.sample \
     --num_repetitions 3
 ```
 
-The script uses the latest checkpoint in `checkpoints/` and the EMA weights. The code README covers the other applications: in-betweening, joint-level editing, motion expansion, and driving a rigged mesh with the result.
+The sampler loads the EMA weights of the latest checkpoint in `checkpoints/`. It writes the motions (`.npy`, shape `(60, J, 12)` for a skeleton with `J` joints) and skeleton renders (`.mp4`) to `outputs/unimate_uniml3d_f60_v2/samples/`. In-betweening, joint-level editing, motion expansion and driving a rigged mesh are documented in the code README.
+
+## Training
 
-### Training
+| | |
+|---|---|
+| **Optimizer** | AdamW, learning rate 1e-4, betas (0.9, 0.99), weight decay 1e-5, gradient clipping at 1.0 |
+| **Schedule** | Linear warmup over the first 3% of steps, then cosine decay to 5% of the peak rate |
+| **Loss** | Masked L2 flow-matching loss + 0.5 × geodesic rotation loss + 0.1 × velocity smoothness loss |
+| **EMA** | Decay 0.9999; used at inference |
+| **Batch size** | 16 per GPU |
+| **Data** | 60-frame windows at 30 fps; joint addition, joint removal, pooling and perturbation augmentations |
 
-To resume training from a released checkpoint:
+| Model | Config (code repository) | GPUs | Training time |
+|---|---|---|---|
+| `unimate_uniml3d_f60_v2` | `configs/uniml3d_60frames_graph_adaln_v2.json` | 8× H100 | about 22 hours |
+| `unimate_uniml3d_f60_v2_full_cross_attn` | `configs/uniml3d_60frames_full_cross_attn_v2.json` | 8× H100 | about 36 hours² |
+| `unimate_uniml3d_f60_preview` | `configs/uniml3d_60frames_graph_adaln.json` | 6× H100 | about 23 hours |
+
+² Resumed once from the step-60k checkpoint.
+
+Resume from a released checkpoint, or train from scratch:
 
 ```bash
 bash scripts/run_train.sh configs/uniml3d_60frames_graph_adaln_v2.json -- \
     --resume outputs/unimate_uniml3d_f60_v2/checkpoints/checkpoint_step_100000.pt
-```
 
-To train `unimate_uniml3d_f60_v2` from scratch:
-
-```bash
 accelerate launch --num_processes 8 -m unimate.training.train \
     --config configs/uniml3d_60frames_graph_adaln_v2.json
 ```
@@ -105,12 +124,13 @@ accelerate launch --num_processes 8 -m unimate.training.train \
 
 ```
 config.json                        index of the released models
+LICENSE
 <model>/
   config.json                      resolved training configuration; read by inference
   dataset_stats.npy                feature normalization statistics
   checkpoints/
-    checkpoint_step_<N>.pt         model and EMA weights, optimizer and scheduler state; saved every 10k steps
-  logs/                            TensorBoard training curves
+    checkpoint_step_<N>.pt         model and EMA weights, optimizer and scheduler state; every 10k steps
+  logs/                            TensorBoard curves, one event file per training job
   samples/
     step_<NNNNNN>/                 motions generated during training (step_000000: before training)
       <object_type>-<i>_fk.mp4     joints from forward kinematics of the predicted rotations
@@ -119,13 +139,14 @@ config.json                        index of the released models
 
 ## Limitations
 
-- **Clip length:** clips are 60 frames at 30 fps. The expansion application chains clips into longer motion.
-- **Skeletons:** a skeleton must go through the data pipeline (stages 1-4) before the model can animate it. Skeletons with more joints than the model's limit (70 for v2) are outside its training range.
-- **Data version:** UniML3D is actively maintained. Captions and annotations change between releases, so pin the dataset revision a model was trained on if you need to reproduce it. Models trained on later releases will be published here with their data version noted.
+- **Clip length:** a sample is a fixed 60-frame window (2 seconds). Longer motion requires chaining samples with the expansion application.
+- **Joint count:** the v2 models are trained on skeletons with at most 70 joints, which excludes four Truebones species (Bear, Centipede, Monkey, Dragon); the preview model's limit is 60. Skeletons with more than 71 joints (61 for the preview model) cannot be sampled.
+- **New skeletons:** there is no skeleton-only input. A new rig must be converted into a feature directory with `data_process/scripts/run_preprocess_char.sh`, and needs at least one animation clip.
+- **Text:** every training caption has the subject "An object"; the skeleton determines the character. Prompts should describe the motion only.
 
 ## License
 
-The UniMate code is released under the MIT License. The training data remain governed by the licenses of their original sources: Adobe's Mixamo terms of use, the per-object licenses of Objaverse-XL, and the commercial license of the [Truebones](https://truebones.com) ZOO pack. Please review these terms before using the models.
+The checkpoints and the UniMate code are released under the [MIT License](LICENSE). The training data remain under their source licenses: Adobe's Mixamo terms of use, the per-object licenses of Objaverse-XL, and the commercial [Truebones](https://truebones.com) ZOO license. Review these terms before using the models.
 
 ## Citation
 

config.json

@@ -17,6 +17,20 @@
       "status": "recommended",
       "dataset_revision": "faaa81773b315247b03f183548e3898dbec2ce80"
     },
+    "unimate_uniml3d_f60_v2_full_cross_attn": {
+      "dataset": "Linzhan/UniML3D",
+      "train_config": "configs/uniml3d_60frames_full_cross_attn_v2.json",
+      "attention": "full",
+      "text_cond": "cross_attn",
+      "num_layers": 10,
+      "latent_dim": 512,
+      "num_frames": 60,
+      "fps": 30,
+      "steps": 100000,
+      "recommended_checkpoint": "checkpoints/checkpoint_step_100000.pt",
+      "status": "variant",
+      "dataset_revision": "faaa81773b315247b03f183548e3898dbec2ce80"
+    },
     "unimate_uniml3d_f60_preview": {
       "dataset": "Linzhan/UniML3D",
       "train_config": "configs/uniml3d_60frames_graph_adaln.json",

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unimate_uniml3d_f60_v2_full_cross_attn/config.json

@@ -0,0 +1,116 @@
+{
+    "experiment": {
+        "name": "uniml3d_60frames_full_cross_attn_v2",
+        "output_dir": "./outputs/uniml3d_60frames_full_cross_attn_v2"
+    },
+    "truebones": {
+        "type": "truebones",
+        "path": "dataset/features/truebones",
+        "objects_subset": "all"
+    },
+    "mixamo": {
+        "type": "mixamo",
+        "path": "dataset/features/mixamo"
+    },
+    "objaverse": {
+        "type": "objaverse",
+        "path": "dataset/features/objaverse",
+        "objects_num": -1,
+        "filter_object": false
+    },
+    "dataset": {
+        "dataset_list": [
+            "truebones",
+            "mixamo",
+            "objaverse"
+        ],
+        "topology_condition_type": "tpos",
+        "motion_repr": "unimate",
+        "max_motion_length": 60,
+        "max_joints": 71,
+        "min_joints": 5,
+        "max_depth": 19,
+        "feature_len": 12,
+        "use_dataset_stats": true,
+        "balanced_stats": true,
+        "tie_std": false,
+        "sampler_alpha": 0.5,
+        "sampler_dataset_alpha": 0.25,
+        "test_split_ratio": 0.0,
+        "split_seed": 42,
+        "use_addition_aug": true,
+        "use_removal_aug": true,
+        "use_pooling_aug": true,
+        "use_perturbation_aug": true,
+        "ground_motion_height": true,
+        "realign_feature": true
+    },
+    "model": {
+        "attention": "full",
+        "text_cond": "cross_attn",
+        "latent_dim": 512,
+        "ff_size": 2048,
+        "num_layers": 10,
+        "num_heads": 8,
+        "dropout": 0.0,
+        "use_spectral_rope": true,
+        "max_freqs": 8,
+        "use_signnet": true,
+        "cond_mode": "text",
+        "cond_mask_prob": 0.1,
+        "use_joint_name_emb": true,
+        "use_graph_emb": false,
+        "use_depth_emb": true,
+        "concat_parent_features": true,
+        "num_tpos_queries": 4,
+        "inject_tpos_to_adaln": true,
+        "use_graph_attn_bias": true,
+        "share_graph_attn_bias": false,
+        "gradient_checkpointing": false,
+        "text_encoder_type": "t5",
+        "text_encoder_version": "google/flan-t5-base"
+    },
+    "scheduler": {
+        "diffusion_steps": 100,
+        "timestep_respacing": "",
+        "rescale_timesteps": false,
+        "noise_schedule": "cosine",
+        "scale_beta": 1.0,
+        "predict_xstart": true,
+        "learn_sigma": false,
+        "sigma_small": true
+    },
+    "training": {
+        "batch_size": 16,
+        "num_workers": 8,
+        "seed": 10,
+        "diff_model": "flow",
+        "learning_rate": 0.0001,
+        "weight_decay": 1e-05,
+        "adam_beta1": 0.9,
+        "adam_beta2": 0.99,
+        "max_grad_norm": 1.0,
+        "gradient_accumulation_steps": 1,
+        "warmup_ratio": 0.03,
+        "min_lr_ratio": 0.05,
+        "num_epochs": null,
+        "num_steps": 100000,
+        "log_interval": 50,
+        "save_interval": 10000,
+        "lambda_geo": 0.5,
+        "lambda_smooth": 0.1,
+        "use_ema": true,
+        "ema_decay": 0.9999,
+        "balanced": true
+    },
+    "sampling": {
+        "model_path": null,
+        "output_dir": null,
+        "seed": 10,
+        "object_type": null,
+        "num_samples": 2,
+        "num_repetitions": 3,
+        "device": "cuda",
+        "cfg_scale": 3.0
+    }
+}

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