Marionette: Predicting World States, Rendering Geometry, Painting Appearance
By Zian Meng, Zhen Li, Chuanhao Li, Qiang Li, Kaipeng Zhang
"Marionette predicts explicit 3D articulated states, renders geometry in closed form, and uses control-conditioned video diffusion for appearance, enabling long-horizon consistency and direct controllability in game agents."
Abstract
Interactive game world models typically autoregress visual observations directly in pixel or latent space, forcing structured properties such as pose, geometry, and occlusion to be implicitly maintained by the same generative sequence. Over long horizons, errors in these latent world properties accumulate, making consistency and controllability fragile. We explicitly model the evolving world state, delegate exact geometric computation to a fixed, zero-parameter renderer, and leave the neural model to synthesize appearance. We instantiate this idea as Marionette, a world model for interactive games with articulated characters. First, a two-stage autoregressive dynamics model predicts an explicit and interpretable 276-dimensional 3D world state comprising multi-entity articulated skeletons, metric root trajectories, and rotations. Second, a zero-parameter graphics bridge converts the predicted state into pose-control videos, computing world-space geometry and occlusion in closed form. Third, a control-conditioned video-diffusion observation model synthesizes photorealistic RGB observations from the resulting structured controls. Our experiments establish two properties of Marionette. First, the predicted world state is directly controllable. Forcing a mismatched action stream changes root-aligned joint error by 31% across 48 held-out segments. Second, long-horizon behaviour is determined in the state, and can be repaired there. Left free, the two generated characters drift to 21.2 m apart (recorded sessions stay near 5 m) and a third of frames show ground penetration. Two rules imposed on the explicit state, a terrain collider and a separation cap, cut penetration by 66% and keep the pair engaged, with no change to the observation model. Routing appearance through the predicted state costs no fidelity we can detect, at an FVD of 831 against 799 for recorded pose.
Technical Analysis & Implementation
Overview§
Marionette decouples world modeling into three explicit stages: (1) a two-stage autoregressive dynamics model predicts a compact 276-dimensional world state (skeletons, root trajectories, rotations), (2) a zero-parameter graphics bridge converts the state into pose-control videos via closed-form rendering, and (3) a video diffusion model synthesizes photorealistic RGB from those controls. This design avoids the implicit maintenance of geometry and occlusion in latent autoregressive sequences, instead making the world state directly inspectable and repairable.
State Space and Dynamics§
Let \(s_t \in \mathbb{R}^{276}\) denote the world state at time \(t\), encompassing multi-entity articulated skeletons, metric root trajectories, and rotations. A two-stage autoregressive model predicts \(s_t\) from \(s_{t-1}\) and action \(a_t\): $$\hat{s}_t = g(s_{t-1}, a_t; \theta)$$ where \(g\) is a residual predictor, and the two stages break the prediction into root motion and joint/spine autoregression.
Graphics Bridge§
The zero-parameter bridge \(\mathcal{R}\) maps state to structured control video \(c_t\): $$c_t = \mathcal{R}(\hat{s}_t)$$ It computes skeletal skinning, world-space geometry, and occlusion in closed form, producing deterministic pose-encoding videos that guide the diffusion model.
Diffusion Observation Model§
A video diffusion model \(p_\phi(o_t | c_t, o_{<t})\) generates RGB observations \(o_t\) conditioned on the control video. Training minimizes the standard diffusion loss plus a state regression loss: $$\mathcal{L} = \mathbb{E}_{t}\left[\|\epsilon - \epsilon_\phi(o_t, c_t, t)\|^2\right] + \lambda \|s_t - \hat{s}_t\|^2$$
The explicit state permits rule-based intervention: a terrain collider and separation cap reduce ground penetration by 66% and keep agents engaged, without retraining the observation model.
Implementation Sketch§
Below is a simplified PyTorch outline of the pipeline:
torch.nn as nn
class Marionette(nn.Module):
def __init__(self, state_dim=276, hidden_dim=512):
super().__init__()
self.dynamics = nn.Sequential(
nn.Linear(state_dim + action_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, state_dim)
)
self.bridge = ZeroParamRenderer() # fixed function
self.diffusion = VideoDiffusionModel()
def forward(self, state_prev, action, obs):
state_pred = self.dynamics(torch.cat([state_prev, action], dim=-1))
control_video = self.bridge(state_pred) # no parameters
obs_recon = self.diffusion(obs, control_video) # returns noise prediction
return state_pred, obs_reconWhy It Matters§
By separating geometry from appearance, Marionette achieves long-horizon stability (reducing root drift from 21.2m to ~5m with simple rules), enables direct action-space control (31% joint error change), and matches recorded-pose FVD (831 vs 799), indicating no fidelity loss from the explicit state bottleneck.
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