Mover360: Controllable Object Manipulation in 360° Panoramic Images

Haoyi Zhong; Fang-Lue Zhang; Andrew Chalmers; Taehyun Rhee

cs.CVAug 24, 2026

Mover360: Controllable Object Manipulation in 360° Panoramic Images

Authors: Haoyi Zhong, Fang-Lue Zhang, Andrew Chalmers, Taehyun Rhee

Organizations: Victoria University of Wellington · University of New South Wales · The University of Melbourne

Abstract

We present Mover360, a controllable object manipulation framework for 360° images. Unlike perspective images, 360° images in equirectangular projection (ERP) exhibit horizontal wrap-around, latitude-dependent distortion, and global scene continuity, which makes object-level edits difficult for existing perspective editors to produce and for users to specify. To address this, Mover360 centers on object Translation (relocating a specified object within an existing panorama) while supporting reference-guided Insert and Remove as auxiliary tasks. Its interface unifies point-, bbox-, and mask-guided control by encoding each task into a fixed prompt and a compact, ERP-aligned instruction map. In the default point mode, a single click relocates an object, allowing the model to infer a plausible size, support, and illumination using panoramic context and an auxiliary depth condition. Structurally, Mover360 is a lightweight adaptation of a pretrained diffusion transformer. To generate paired supervision, we construct a UE5 data-generation pipeline with surface-aware object placement and randomized illumination, yielding large-scale paired data and a dual-domain benchmark of synthetic and real panoramas with ground truth for all three tasks. Across both test domains and two evaluation protocols, Mover360 outperforms strong baselines for perspective editing, insertion, and inpainting in reconstruction fidelity, semantic consistency, and distributional quality. Code and our benchmark dataset are available at https://zhonghaoyi.github.io/Mover360/.

Figures & tables

Figure 1: Anatomy of one Mover360 editing tuple from the UE5 test set (top) and from the real captured test set (bottom). Each tuple contains a source-state panorama, a target-state panorama, an object-absent background panorama, and a perspective reference image, and therefore supervises all three tasks: Translation (source $\,\to\,$ target), Remove (source $\,\to\,$ background), and Insert (background $\,+\,$ reference $\,\to\,$ object present). The left column overlays all three guidance granularities of the instruction map: fine object masks (contours), bbox regions (translucent rectangles), and the Gaussian target point placed at the bottom-center of the target box (blue dot), with the source in red and the target in green. Note that the source object of the real example straddles the horizontal wrap-around boundary of the ERP.

Figure 2: Examples from the Mover360 data generation pipeline. The top two rows show randomized nearby lamps, including variations in intensity, temperature, scattering or beam angle, and position. In the third row, the first two examples show outdoor/global light-source variation, and the last example shows one object path captured by three panoramic cameras.

	Train (UE5)	Test (UE5)	Test (real)
Editing samples	12,150 seq.	210	50
Sampled pairs per epoch	60,750	–	–
Unique objects	608	48	15
Ground truth for	Translation / Remove / Insert
Object size (% of sphere)	0.69 [0.27, 1.72]	1.04 [0.40, 2.15]	0.79 [0.49, 1.72]
Translation ( ∘ )	23.4 [11.9, 42.3]	31.4 [18.0, 47.5]	50.9 [34.2, 103.0]

Table 1: Statistics of the Mover360 training corpus and the dual-domain test benchmark. Distribution rows report median [interquartile range], computed from the ERP object masks: object size is the solid-angle fraction of the sphere, translation is the wrap-aware great-circle displacement of the mask centroid (sampled training pairs for the corpus, source-to-target pairs for the test sets), and elevation is the centroid latitude.

Figure 3: Distributions of sphere-aware statistics for the Mover360 training corpus and the two test sets: object size as a percentage of the viewing sphere (log scale), great-circle translation distance, and object elevation. The two synthetic sets share similar distributions with a wider size range, while the real test set deliberately stresses long-range translations (median

50.9^{\circ}

, with a quarter of the moves beyond

103^{\circ}

Figure 4: Overview of Mover360. The framework takes an input ERP panorama, a fixed task prompt, an ERP-aligned instruction map, an auxiliary DA 2 depth map, and an optional reference image for insertion. Condition latents are concatenated and patchified before being processed by the Flux2-Klein diffusion transformer. We adapt the native 4D RoPE coordinates to distinguish text tokens, noisy target panorama tokens, and multiple conditioning image slots. LoRA adapters are trained while the reference branch is disabled for translation and remove. VAE encoding and decoding are wrapped with horizontal circular pad-and-unpad to better respect panorama periodicity.

Figure 5: Qualitative comparison for the Translation task. Baselines often leave residual content at the source location, distort the moved object, or produce less plausible contact with the target surface. Mover360 uses the point or region instruction to move the object while preserving the surrounding panorama.

Figure 6: Qualitative comparison for the Insert task. Reference-guided baselines may copy viewpoint-specific appearance or produce inconsistent object scale. Mover360 better adapts the inserted object to the target region and the panorama context.

Figure 7: Qualitative comparison for the Remove task. LaMa is a strong specialized inpainting baseline, but Mover360 performs removal within the same unified framework used for translation and insertion.

Figure 8: Qualitative comparison under the perspective protocol . Rows show Translation (UE5), Insert (real), and Remove (UE5); columns show the input panorama with the instruction regions overlaid (source in red, target in green, reference inset for Insert), the square perspective view given to the baseline with the provided target box, the baseline’s edited view, the baseline’s final panorama after re-projection, Mover360 (mask), and the ground truth. For Translation, the baseline crops the source-removed panorama. Mask-conditioned baselines struggle with global consistency due to local cropping: the relocated car and inserted backpack lack the cast shadows and reflected illumination seen in Mover360 and ground truth. For Remove, the baseline fails to eliminate the long shadow extending beyond its perspective crop, whereas Mover360 removes the entire shadow.

		UE5 test set (210)						Real test set (50)
Task	Method	FAED $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	DINOv3 $\uparrow$	FID $\downarrow$	FAED $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	DINOv3 $\uparrow$	FID $\downarrow$
Translation	DragAnything [ 29 ]	8.797	12.68	0.351	0.607	0.288	208.0	24.362	10.86	0.422	0.679	0.215	240.0
	AnyDoor [ 4 ]	1.157	22.18	0.571	0.352	0.687	116.0	4.167	22.00	0.673	0.345	0.611	167.0
	Paint-by-Example [ 33 ]	1.504	21.75	0.564	0.337	0.437	133.0	1.888	22.29	0.626	0.352	0.454	199.0
	OmniPaint [ 37 ]	1.196	22.02	0.696	0.318	0.662	117.0	2.340	21.43	0.662	0.323	0.656	167.0
	Insert-Anything [ 19 ]	0.344	24.23	0.809	0.297	0.739	108.9	0.229	26.23	0.802	0.279	0.718	151.0

Table 2: Quantitative comparison under the ERP protocol , where baselines operate directly on equirectangular panoramas, reported separately on the UE5 (210 samples per task) and real (50 samples per task) test sets. FAED and PSNR are computed on full ERP panoramas; SSIM, LPIPS, DINOv3 similarity, and FID are computed on perspective views centered on the edit region. Mover360 (point) uses only a target point. All baselines receive the known-size target bbox or mask.

		UE5 test set (210)						Real test set (50)
Task	Method	FAED $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	DINOv3 $\uparrow$	FID $\downarrow$	FAED $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	DINOv3 $\uparrow$	FID $\downarrow$
Translation	AnyDoor [ 4 ]	0.451	23.39	0.798	0.291	0.727	103.7	0.307	26.56	0.767	0.277	0.675	140.6
	Paint-by-Example [ 33 ]	0.559	23.30	0.817	0.302	0.513	131.6	0.459	25.42	0.768	0.303	0.561	176.4
	Insert-Anything [ 19 ]	0.323	24.52	0.822	0.256	0.769	97.8	0.214	27.88	0.784	0.250	0.727	132.4
	OmniPaint [ 37 ]	0.461	23.36	0.813	0.285	0.692	107.4	0.245	27.17	0.784	0.268	0.653	135.6
	Mover360 (point)	0.265	24.25	0.830	0.270	0.721	97.8	0.294	26.04	0.781	0.274	0.669	144.5

Table 3: Quantitative comparison under the perspective protocol , where each baseline edits a locally undistorted, adaptively framed square perspective view (FOV clipped to

[35^{\circ},150^{\circ}]

) and only the edited region is re-projected into the panorama. This adaptation is strictly favorable to the baselines. Mover360 operates natively on the ERP; for fairness, its output pixels outside the perspective-view crop are likewise replaced by the input panorama, so its FAED and PSNR differ from Table 2 while the perspective-view metrics are unchanged. DragAnything is omitted because its video-based dragging does not transfer to this protocol.

		UE5 test set (210)						Real test set (50)
Task	Method	FAED $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	DINOv3 $\uparrow$	FID $\downarrow$	FAED $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	DINOv3 $\uparrow$	FID $\downarrow$
Translation	Mover360 w/o depth	0.351	24.42	0.813	0.275	0.708	100.0	0.374	26.53	0.752	0.297	0.655	148.5
	Mover360 (point)	0.336	24.14	0.830	0.270	0.721	97.8	0.357	26.82	0.781	0.274	0.669	144.5
	Mover360 (bbox)	0.314	25.08	0.836	0.255	0.747	95.5	0.356	26.85	0.823	0.249	0.704	131.0
	Mover360 (mask)	0.250	25.43	0.868	0.197	0.780	82.9	0.186	29.35	0.841	0.181	0.717	121.6
Insert	Mover360 w/o depth	0.438	23.42	0.821	0.355	0.671	112.0	0.493	25.59	0.734	0.335	0.625	174.1

Table 4: Ablation study on the auxiliary depth condition and the spatial-instruction granularity (point/bbox/mask), reported separately on the UE5 and real test sets. FAED and PSNR are computed on full ERP panoramas; the remaining metrics are computed on perspective views centered on the edit region. The w/o-depth variant uses point guidance for Translation and Insert and bbox guidance for Remove.

Figure 9: Failure cases of Mover360. Top: translating a large-area light source. The emitter is relocated to the target point, but the wide illumination it casts is not re-synthesized: the brightly lit region anchored to the original position survives (dashed boxes), decoupling the moved source from the scene lighting; compact emitters do not trigger this failure. Bottom: translating a building-scale structure. The building is relocated, but additional structures absent from the input are hallucinated near the target (dashed box).

Figure S1 : Additional Translation results. Each pair shows the input panorama with the source region, the motion arrow, and the Gaussian target point overlaid, and the edited panorama with a magnified view of the target region (dashed box).

Figure S2 : Additional Insert results. Each pair shows the input panorama with the reference object (inset) and the target point overlaid, and the edited panorama with a magnified view of the inserted object (dashed box).

Figure S3 : Additional Remove results. Each pair shows the input panorama with the source object marked, and the edited panorama with a magnified view of the completed background (dashed box).

Figure S4 : Qualitative ablation of the auxiliary depth condition under the default point guidance. Asked to relocate the object to the target point (arrow), the w/o-depth model renders it at an implausibly large scale, whereas the depth-conditioned model infers a projected size consistent with the scene depth at the target location (insets).

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Interactive panoramic video generation aims to synthesize immersive 360\textdegree{} videos that remain visually coherent while following user-specified camera trajectories during exploration. However, progress is limited by a coupled data-and-model gap: existing panoramic video datasets are often short, weakly annotated, or lack camera trajectories, while existing camera-controlled video generation models are designed for perspective videos and do not directly support panoramic geometry. In this paper, we introduce MUGEN and Wan360 to address these limitations. MUGEN is a large-scale real-world panoramic video dataset tailored to interactive 360-degree world exploration, comprising over 1,300 hours of at least 4K panoramic videos with rich semantic and geometric annotations. Built on MUGEN, we further present Wan360, a camera-controllable interactive panoramic video generation model. Panoramic videos are commonly represented by EquiRectangular Projection (ERP), which unfolds a spherical 360-degree view into a rectangular frame with cyclic longitude seams and pole distortions. To this end, Wan360 introduces three parameter-free ERP-aware components: periodic longitude RoPE for seam-consistent positional encoding, ERP-aware padding for reducing boundary artifacts, and random roll yaw for consistent learning. For camera control, Wan360 uses a panoramic Plücker embedding that represents camera motion with ERP rays rather than perspective pinhole rays. Experiments show that MUGEN serves as a data foundation for panoramic world exploration, and that Wan360 enables high-quality, temporally coherent, camera-controllable 360-degree video generation.

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