Motif Vision Encoder is a unified image + video self-supervised vision encoder on a ViT backbone. A single 3D-convolutional tokenizer ingests both modalities — an image is a 1-frame clip (T=1), a video is T>1 — so the same weights produce dense patch-level features and a language-aligned global (CLS) representation.

Trained on ~1/3 the data of DINOv3 (0.5B vs 1.7B samples), it still reaches competitive performance across image and video benchmarks — and leads on DAVIS video tracking.

Point tracking on a video clip: Motif vs V-JEPA 2.1

Point tracking on a video clip (top: Motif, bottom: V-JEPA 2.1) — a query point propagated across frames by patch-feature cosine similarity. Motif tracks the subject more reliably than V-JEPA 2.1.

Architecture: ViT-7B (embed 4096 / depth 40 / heads 32), patch 16, 3D axial RoPE (base=100), SwiGLU FFN, LayerScale, per-head QK-norm, gated attention, 4 register tokens.
Tokenizer: Conv3d(kernel=stride=(tubelet, patch, patch)) — image (B,3,H,W) → T=1, video (B,T,3,H,W). Token layout [CLS] + [register × 4] + [patch × N].

Usage

The model ships a self-contained modeling_motif_vision_encoder.py, so it loads with trust_remote_code=True.

Image

python

import torch
from transformers import AutoImageProcessor, AutoModel
from transformers.image_utils import load_image

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = load_image(url)

repo = "Motif-Technologies/Motif-Vision-Encoder"
processor = AutoImageProcessor.from_pretrained(repo)
model = AutoModel.from_pretrained(repo, trust_remote_code=True, dtype=torch.bfloat16).to("cuda").eval()

inputs = processor(images=image, return_tensors="pt").to(model.device, torch.bfloat16)
with torch.inference_mode():
    outputs = model(**inputs)

outputs.last_hidden_state   # (1, 1 + 4 + N, 4096)  CLS + registers + patch tokens
outputs.pooler_output       # (1, 4096)             global (CLS) representation

patch_tokens = outputs.last_hidden_state[:, 5:, :]   # (1, N, 4096), N = (H/16)*(W/16)

The processor resizes the shorter side to 512, center-crops to 512×512, and normalizes with ImageNet mean/std (BICUBIC). H/W must be multiples of 16.

Video

An image is a 1-frame clip; a video is the same call with a (B, T, 3, H, W) tensor. Apply the same per-frame transform (resize → center-crop → ImageNet norm) and stack over time:

python

import torch

video = torch.randn(1, 8, 3, 256, 256, device="cuda", dtype=torch.bfloat16)  # (B, T, 3, H, W)
with torch.inference_mode():
    outputs = model(pixel_values=video)

Model details

Motif Vision Encoder architecture: image and video inputs, patch embedding, 40-block transformer stack, and transformer block internals


Backbone	ViT-7B, patch 16, embed 4096, depth 40, heads 32, SwiGLU
Register tokens	4
Position encoding	3D axial RoPE (T,H,W), `base=100.0`
Video tokenizer	3D Conv, tubelet size 2
Precision	bf16 weights
Training	DINO + iBOT + KoLeo self-distillation, Gram anchoring, contrastive caption alignment
Training data	~0.47B samples — 448.6M images (96%) + 18.5M video clips (4%)

Outputs (BaseModelOutputWithPooling): last_hidden_state (B, 1+4+N, 4096), pooler_output (B, 4096).

Evaluation

Compared against the strongest publicly reported self-supervised / vision backbones. Higher is better for every column. Best comparable value per column in bold, second best underlined.

DAVIS S/M/L follow the DINOv3 protocol (J&F-mean at video short side 420/480, 840/960, 1260/1440 px). V-JEPA 2.1 is not part of the DINOv3 Table 5 tracking benchmark, so only its single-resolution (S) figure is available.

Model	Training data	DAVIS S J&F ↑	DAVIS M J&F ↑	DAVIS L J&F ↑	ImageNet-1K lin. probe ↑	ADE20K mIoU ↑	K400 ↑
Motif Vision Encoder	0.5B	73.8	80.4	83.4	87.4	52.0	87.4
DINOv3	1.7B	71.1	79.7	83.3	88.4	55.9	87.8
Web-DINO	2B	57.2	65.8	69.5	85.9	42.7	86.8
PEcore	5.4B	48.2	53.1	49.8	89.3	38.9	87.9
SigLIP2	10B	56.1	62.3	62.9	89.1	45.4	86.9
OpenCLIP	2B	–	–	–	–	–	–
V-JEPA 2.1	0.022B	69.0	–	–	85.5	47.9	87.7

Protocol: DINOv3-style linear/attentive probes for image tasks; V-JEPA 2-style protocol for video. Baseline DAVIS / ADE20K / K400 figures are taken from the DINOv3 technical report's unified evaluation (Tab. 3, 5, 6) and ImageNet from Tab. 7; OpenCLIP is not in that report and its benchmarks are not reported under a comparable protocol.

Motif is state of the art on DAVIS video tracking at every resolution (74.0 / 80.5 / 83.5 J&F) and stays competitive on the other image and video benchmarks, using roughly 1/3 of DINOv3's training data (~0.5B samples).

Mask propagation: ground truth vs DINOv3 vs Motif

Dense attention and feature-similarity comparison across Motif, DINOv3, V-JEPA 2.1, and SigLIP2

Dense features on a single image (768px). Columns: query point, CLS attention, query-point attention, patch-feature cosine similarity. Motif and DINOv3 keep attention and similarity tightly localized on the queried object, while V-JEPA 2.1 and SigLIP2 are noticeably noisier.

License

Released under the MIT License (see LICENSE). The model was trained on data governed by the respective dataset licenses; downstream users are responsible for compliance with those terms.