---
license: apache-2.0
library_name: transformers
pipeline_tag: image-text-to-text
base_model:
  - Qwen/Qwen3-VL-8B-Instruct
tags:
  - visual-search
  - panoramic
  - embodied-ai
  - spatial-reasoning
---

# Imaginator-8B

The Imaginator from *Beyond Thinking: Imagining in 360° for Humanoid Visual
Search*.

An agent searching a 360° scene sees only a narrow field of view at a time.
The Imaginator looks at the views seen so far and says where in the full
panorama the target probably is — including in the parts nobody has looked at
yet. It does not act. A separate, **frozen** search policy decides what to do
with the guess, so the same Imaginator plugs into any policy without retraining
it.

Trained from Qwen3-VL-8B-Instruct.

| Path in this repo | What it is |
|---|---|
| `Imagine-8B/` | this model |
| `HVS-3B/` | the frozen search policy used for the numbers below, from Yu et al. |

## Input and output

**Input** — every narrow view seen so far, each labelled with the camera
direction it was taken at, plus the instruction:

```
[View 1] viewing direction: (0,0)
<image>
[View 2] viewing direction: (0,-30)
<image>
Human Instruction: look for the black and white striped blanket
Decide your next action.
```

`viewing direction` is `(yaw, pitch)` in degrees: yaw in `[0,360)` measured
clockwise, pitch in `[-90,90]` with positive up. Views accumulate across the
episode; there is one `<image>` per view, in the order listed.

**Output** — a reasoning block that separates what is visible from what is
inferred, then a single predicted target location:

```
<think>[Observed]
- rolled rugs: (342,-4)
- ceiling light: (7,34)
- shopping cart handle: (40,-32)
[Imagined]
- rug sample shelves: (126,-9)
- bedding section: (200,-5)
</think><answer>suggest check(126,-9)</answer>
```

`[Observed]` are landmarks the model can see in the given views, with their
absolute panorama coordinates. `[Imagined]` are landmarks it infers lie outside
them — this is the part that carries the spatial prior. The `<answer>` is one
absolute `(yaw, pitch)` guess at where the target is.

The coordinate is a **proposal, not a detection**. On HOS its top-1 hit rate
under the benchmark tolerance is 39.04%, and the system still reaches 62.75,
because the policy is free to reject a bad guess and keep searching.

Downstream, the harness converts `check(yaw,pitch)` into the relative
`rotate(dyaw,dpitch)` or `submit(yaw,pitch)` form the policy was trained on,
depending on whether the target is already within tolerance of the current
view, and appends it to the policy's turn.

## Results

H\*Bench success rate, from the paper. The policy is identical in both rows and
frozen; the only difference is whether it receives the Imaginator's guess.

| | HOS | HPS |
|---|---|---|
| HVS-3B | 48.04 | 24.12 |
| **HVS-3B + Imaginator-8B** | **62.75** | **39.38** |

## Usage

vLLM cannot serve a model from a subdirectory of a repo, so fetch it first:

```bash
hf download jdzhang0929/Imaginator --include "Imagine-8B/*" --local-dir ./checkpoints
vllm serve ./checkpoints/Imagine-8B --port 8001 --served-model-name imaginator
```

With transformers the subfolder is addressable directly:

```python
from transformers import AutoModelForImageTextToText, AutoProcessor

model = AutoModelForImageTextToText.from_pretrained(
    "jdzhang0929/Imaginator", subfolder="Imagine-8B",
    dtype="auto", device_map="auto")
processor = AutoProcessor.from_pretrained(
    "jdzhang0929/Imaginator", subfolder="Imagine-8B")
```

Greedy decoding, `max_tokens=4096`. The full two-model loop — view accumulation,
coordinate conversion, and the multi-hypothesis injection used in the paper —
is in the code repo.

## Training

Two stages. Stage 1 pretrains on 1.92M pseudo-labelled panorama samples
(10,000 steps at batch 192) drawn from Sun360, Matterport3D and DiT360 renders.
Stage 2 is a clean SFT on 4,223 H\*Bench trajectories, 2 epochs at batch 64,
lr 1e-5 cosine.

Stage 2 is reproducible from public data: the trajectories are at
[jdzhang0929/Imagine-in-360-Dataset](https://huggingface.co/datasets/jdzhang0929/Imagine-in-360-Dataset)
and the images they reference ship with H\*Bench.

## Data separation

Evaluation panoramas were compared against the stage-2 SFT panoramas
exhaustively at the pixel level — 857 × 382 pairs, 36 yaw rotations each, under
two criteria (MAE < 2.0 grey levels for "same photo", Pearson r ≥ 0.90 for
"same viewpoint"). Two overlaps surfaced, both inherited from the original
H\*Bench split, and both are removed from the released SFT trajectories.
Against the 40,453 stage-1 pseudo-label panoramas the same sweep found nothing
at either threshold; the global maximum correlation was 0.8965.

## Limitations

- English instructions only.
- Top-1 coordinate accuracy is 39.04% (HOS) / 26.12% (HPS). Treat the output as
  a prior over where to look, not as a localization result.
- Rotation-only search from a fixed viewpoint; no translation.
- Coordinates assume the equirectangular convention above. A different yaw
  origin or pitch sign will silently produce plausible but wrong guesses.

## Citation

```bibtex
@article{imagining360,
  title  = {Beyond Thinking: Imagining in 360{\deg} for Humanoid Visual Search},
  author = {Zhang, Jingdong and others},
  year   = {2026}
}
```

Built on [H\*Bench](https://huggingface.co/datasets/humanoid-vstar/hstar_bench)
and the HVS models from Yu et al.